Software tools for visualization and analysis of human brain data for language mapping

BackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

Motivation: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.Results: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus. The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis.Availability and Implementation: SSBD is accessible at http://ssbd.qbic.riken.jp.Contact:sonami@riken.jp

Next-generation sequencing (NGS) techniques produce high quantities of DNA and RNA sequencing data, enabling variation analysis at a whole genome level. Due to the massive availability of NGS data, the processing and analysis now constitute a serious challenge for research in life sciences. This necessitates the development of novel, user-friendly and scalable software tools, which are able to jointly handle large NGS data sets in order to break the bottleneck that has already moved from data generation to data analysis in high-throughput biology. Here we introduce a highly efficient NGS analysis and visualization software tool (Rikurator), which is designed for researchers who are struggling with NGS data analysis and may not have access to a dedicated bioinformatics team. Rikurator can be applied to a multitude of genomic analysis tasks, including discovery of pathogenic mutations in familial or sporadic sample sets, analysis of structural variants and somatic characterization of cancer genomes. To these ends the software implements interactive variant filtering and annotation methods based on sample comparison, control data (e.g. ExAC), amino acid changes and quality scores, which provide an intuitive view at the studied samples. Shared candidate genes in a set of dozens of individuals can be discovered in a matter of minutes with an ordinary laptop computer. Importantly, the software is able to analyze hundreds of whole-genome sequenced samples using only a modest amount of RAM. The tool also visualizes and integrates genes, variants (VCF file format), sequence reads (BAM) and user defined features (BED/GFF). In addition, Rikurator is not limited to human studies, supporting reference genomes and genome annotations in standard formats. The software supports targeted (e.g. exome), whole-genome, RNA, Oxford Nanopore and PacBio sequencing data. Rikurator can be run on Windows, Linux and Mac, and it will be freely available after publication. Citation Format: Riku Katainen, Veli Mäkinen, Lauri A. Aaltonen, Esa Pitkänen. Fast and scalable software for comparative variant analysis and visualization of massive next-generation sequencing data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5281.

The adoption of manufacturing big data system (MBDS) is anticipated to enhance the competitiveness of small and medium-sized manufacturing firms (SMMFs), as massive amount of big data proliferate in the field of manufacturing industries. Several professionals argue that it is necessary to take an attentive approach in designing appropriate MBDS for SMMFs. This study examines appropriate design options, based on the information design theory, product-service system theory, and contingency theory, which would elevate the adoption probability of MBDS of Korean SMMFs. An empirical analysis of 191 Korean SMMFs reveals the following results. First, SMMFs put similar degree of importance on the entire design attributes of MBDS from big data gathering to information visualization. Second, SMMFs prefer payment per use of low-cost MBDS that provides more simplified information (e.g., on-spot monitoring and 2 dimension visualization) and utilizes open cloud as well as open telecommunication network. However, the firms want to have an independent and sophisticated MBDS that includes professional big data analysis and visualization software tool.

GumTree: Data reduction

Having a solid theoretical foundation is essential for designing an effective software visualization (SV) tool. Despite the decades of developing different SV tools, there are still doubts about their effectiveness. Furthermore, learner engagement plays an important role in building a successful SV tool. In programming education, the problem of the high failure rates among students is still unresolved. Therefore, there is a need to understand the theories behind the exciting SV tools from the engagement perspective in order to have a road map for future tool construction. Yet the factors influencing learner engagement in SV tools are still unclear. This study set out to determine how to develop an SV design model to enhance student engagement in an introductory programming course. A systematic literature review (SLR) was used to obtain an overview of the current theoretical foundation used. The search identified a total of 432 papers between 2011 and 2017. This study examined 58 papers selected based on a well-defined selection process. In this paper, the contribution in constructing the protocol for SLR is presented as well as the preliminary results of the study. The researchers were in the process of data extraction phase to address the research questions. The expected outcomes of this review became the identification of a theoretical background used to construct and explain engagement in software visualization. The expected output of this study was a list of the factors that have a positive impact on learner engagement in SV tools.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

Frequently, at scientific meetings proteome researchers meet after the formal program has ended to discuss the state of the field and their own situation over some drinks. Typically these discussions center around instrumentation, primarily mass spectrometers, with a faster scan rate, higher mass accuracy or sensitivity, and the results generated by these powerful instruments. Statements like, “this is the future,” “this will revolutionize proteomics,” or “this has changed what we do in the lab” can be heard around the table. These sentiments are certainly justified. The progress in mass spectrometric instrumentation in the short history of proteomics has been nothing but astounding. Just a decade ago virtually all proteomic data were generated on ion trap instruments with low resolution, low mass accuracy, and limited scan speed and sensitivity, compared with the instruments presently in common use. Today, a significant fraction of the proteomic data is collected on high performance hybrid mass spectrometers with high scan speed, high mass accuracy and resolution, and high sensitivity of detection. The effects of these dramatic improvements are apparent in the manuscripts being published. However, its importance notwithstanding, data collection is only half of the whole proteomic story. The capacity to annotate, analyze, store, manage, and distribute the many gigabytes of data collected is the other half of the proteomic technology tandem. Long gone are the days when the expert analyst pretended to have manually verified all the data supporting the conclusions in a proteomic paper. Therefore, proteomics today, particularly mass spectrometry-based proteomics, depends as much on computers and the software tools they use as it does on high tech instruments to generate the data. Surprisingly, at the sessions in the bar, no one ever seems to boast about the data analysis part of the proteomic enterprise. Statements such as “ this program just recovered 20% more true positive peptide identification from my data set,” “ this allowed me to analyze my data ten times faster and at a credible FDR,” or “this statistical model allowed me to identify statistically significant quantitative changes in my dataset” are seldom if ever uttered. It is unlikely that things in the realm of electronics and software are inherently less appealing or attractive than high performance devices because the same individuals boasting about their mass spectrometers are likely to also share interesting new iPhone apps. It is certainly also not the case that advances on the software side of proteomics have had a lower impact than publications describing new hardware. In fact some of the most highly cited proteomics papers are about software. I therefore propose the likely controversial thesis that the software world of proteomics is less generally discussed because few can keep up and know about the field in some depth. This view is supported by several observations. First, in a recent study (1), in which a standardized protein sample was analyzed by a number of laboratories, it was clearly apparent that the analysis of the collected data was a major weakness. Second, the adoption of new software tools for proteomic data analysis takes much longer than the adoption of new instruments or wet-lab protocols. To raise the level of awareness in the crucial area of proteomic software tools, Molecular and Cellular Proteomics will publish a series of mini reviews that summarize the current state of specific aspects of this often bewildering field. These are not tutorials, i.e. the reader still will have to figure out how to use the tools and which tools to use. The intent of this series is to discuss the significance of a particular type of analysis, the question(s) addressed, the currently available solutions and their strengths and weaknesses, and the availability and utility of germane software tools or computational resources. We expect that these mini reviews will help proteome researchers to extract more and better information from the data generated by the wonderful instruments available today and to increase the excitement about the topic so that statistical models, search engines, and XML schema can also claim the stature in bar discussions that they deserve.

MSTools—Web based application for visualization and presentation of HXMS data

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.

Development of public toxicogenomics software for microarray data management and analysis

Aims: The aim of this study is to investigate the impact of thresholds on the detection of outliers by comparing the performance of two estimators, namely the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD), at different sample sizes. The study uses simulated data generated from the standard normal distribution to assess how varying thresholds affect the ability of these estimators to detect outliers.
 Study Design: This study employs a quantitative research design. It involves the generation of simulated data, the application of the MCD and MRCD estimators for outlier detection, and the systematic manipulation of thresholds and sample size as independent variables.
 Place and Duration: The study is conducted using computational tools and did not require a physical location.
 Methodology: Simulated data is generated from the standard normal distribution to create a controlled environment for outlier detection experiments. The MCD and MRCD estimators are applied to the simulated data to detect outliers. These estimators are sensitive to deviations from the norm in the data. Different thresholds are systematically applied to the data, and the performance of the estimators is assessed at each threshold level. Thresholds may vary in their extremeness. The study investigates the impact of different sample sizes on outlier detection. This involves using datasets with varying numbers of observations. The r programming language and associated packages are used as the statistical tool for data generation, analysis, and visualization.
 Results: The study's findings indicate that the choice of thresholds in data analysis significantly affects the performance of the MCD and MRCD estimators in outlier detection. If the thresholds used for both estimators are the same, their performance is similar. However, differences emerge when thresholds differ from each other. Higher thresholds are shown to identify less extreme outliers, while lower thresholds are effective at identifying more extreme outliers. These results provide insights into the behavior of these estimators in outlier detection scenarios, shedding light on their sensitivity to threshold choices and sample size.Conclusion: Our study has shed light on the critical interdependencies among threshold choices, sample sizes, and the performance of the minimum covariance determinant (MCD) and minimum regularized covariance determinant (MRCD) estimators in the context of outlier detection. By conducting a systematic exploration in a controlled environment with simulated data, we have gleaned valuable insights that can inform both researchers and practitioners in the field of organizational science research.

Computational approach to study of neuronal impairment is rapidly evolving, as experiments and intuition alone could not explain the complexity of brain system. The increase in an overwhelming amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization, and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals are imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. Therefore, this article seeks to outline the biological databases and analytical tools along with their applications. It's envisaged that knowledge along this line would be a "training recipe" for young talents and guide for professionals and researchers in neuroscience.

Glacial geomorphology and geographic information systems

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided a significant impact on our understanding of biological systems. It contributes to almost all aspects of biomedical research, including studying developmental biology, disease progression, drug effects, and host-parasite relationships. However, the high-throughput data generation presents a challenge for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for gene expression and protein datasets analysis and visualization. GeneCloudOmics allows both microarray or RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides user access to 23 different data analytics and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathways enrichments, evolutionary analyses, pathological analyses and protein-protein interaction (PPI) identifications. The user performs all tasks through an intuitive graphical user interface (GUI) that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. GeneCloudOmics is a one-stop open-source tool for gene exprerssion data analysis and visualization and is freely available at http://combio-sifbi.org/GeneCloudOmics.