scholarly journals Online Tools for Teaching Cancer Bioinformatics

2021 ◽  
Author(s):  
Mason D. Taylor ◽  
Bryn Mendenhall ◽  
Calvin S. Woods ◽  
Madeline E. Rasband ◽  
Milene C. Vallejo ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Molecular Characterization ◽  
Data Sets ◽  
Omics Data ◽  
Statistical Assessment ◽  
Computational Tools ◽  
Data Manipulation ◽  
Biological Phenomena ◽  
Online Tools ◽  
Cancer Bioinformatics

The rise of deep molecular characterization with omics data as a standard in biological sciences has highlighted a need for expanded instruction in bioinformatics curricula. Many large biology data sets are publicly available and offer an incredible opportunity for educators to help students explore biological phenomena with computational tools, including data manipulation, visualization, and statistical assessment.

scholarly journals Protest Event Analysis: Developing a Semiautomated NLP Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Jasmine Lorenzini ◽  
Hanspeter Kriesi ◽  
Peter Makarov ◽  
Bruno Wüest
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Event Analysis ◽  
Validity And Reliability ◽  
Multiple Sources ◽  
Social Scientists ◽  
Extensive Discussion ◽  
Computational Tools ◽  
News Sources

Protest event analysis is a key method to study social movements, allowing to systematically analyze protest events over time and space. However, the manual coding of protest events is time-consuming and resource intensive. Recently, advances in automated approaches offer opportunities to code multiple sources and create large data sets that span many countries and years. However, too often the procedures used are not discussed in details and, therefore, researchers have a limited capacity to assess the validity and reliability of the data. In addition, many researchers highlighted biases associated with the study of protest events that are reported in the news. In this study, we ask how social scientists can build on electronic news databases and computational tools to create reliable PEA data that cover a large number of countries over a long period of time. We provide a detailed description our semiautomated approach and we offer an extensive discussion of potential biases associated with the study of protest events identified in international news sources.

The “core principles” of physiology: what should students understand?

2009 ◽  
Vol 33 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Joel Michael ◽  
Harold Modell ◽  
Jenny McFarland ◽  
William Cliff
Keyword(s):  
Biological Sciences ◽  
Assessment Instrument ◽  
The Core ◽  
Biological Phenomena ◽  
Starting Point ◽  
Constituent Component ◽  
Thinking Tools ◽  
Student Attention

The explosion of knowledge in all of the biological sciences, and specifically in physiology, has created a growing problem for educators. There is more to know than students can possibly learn. Thus, difficult choices have to be made about what we expect students to master. One approach to making the needed decisions is to consider those “core principles” that provide the thinking tools for understanding all biological phenomena. We identified a list of “core principles” that appear to apply to all aspects of physiology and unpacked them into their constituent component ideas. While such a list does not define the content for a physiology course, it does provide a guideline for selecting the topics on which to focus student attention. This list of “core principles” also offers a starting point for developing an assessment instrument to be used in determining if students have mastered the important unifying ideas of physiology.

scholarly journals Quickomics: exploring omics data in an intuitive, interactive and informative manner

2021 ◽  
Author(s):  
Benbo Gao ◽  
Jing Zhu ◽  
Soumya Negi ◽  
Xinmin Zhang ◽  
Stefka Gyoneva ◽  
...  
Keyword(s):  
Modular Design ◽  
Functional Module ◽  
Supplementary Information ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Primary Analysis ◽  
Link Type ◽  
R Shiny ◽  
Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

scholarly journals Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4349 ◽  
Author(s):  
Aristóteles Góes-Neto ◽  
Marcelo V.C. Diniz ◽  
Daniel S. Carvalho ◽  
Gilberto C. Bomfim ◽  
Angelo A. Duarte ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Phylogenetic Analysis ◽  
Complex Networks ◽  
Complex Network ◽  
Bootstrap Method ◽  
Molecular Data ◽  
Data Sets ◽  
Fungal Cell ◽  
Taxonomic Groups ◽  
Inference Methods

Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.

scholarly journals Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 76 ◽  
Author(s):  
Farhana R. Pinu ◽  
David J. Beale ◽  
Amy M. Paten ◽  
Konstantinos Kouremenos ◽  
Sanjay Swarup ◽  
...  
Keyword(s):  
New Zealand ◽  
Systems Biology ◽  
Life Science ◽  
Biological Systems ◽  
Research Community ◽  
Data Sets ◽  
Omics Data ◽  
Omics Integration ◽  
Traditional Approaches ◽  
Multiple Interactions

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

scholarly journals Identifying significantly impacted pathways: a comprehensive review and assessment

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Tuan-Minh Nguyen ◽  
Adib Shafi ◽  
Tin Nguyen ◽  
Sorin Draghici
Keyword(s):  
Pathway Analysis ◽  
Null Hypothesis ◽  
Large Scale ◽  
Data Sets ◽  
Actual Performance ◽  
Large Scale Assessment ◽  
Analysis Methods ◽  
Biological Phenomena ◽  
High Throughput Experiments ◽  
The Given

Abstract Background Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true. Results This article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested. Conclusion Overall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.

scholarly journals Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e89297 ◽  
Author(s):  
Alexander Kaever ◽  
Manuel Landesfeind ◽  
Kirstin Feussner ◽  
Burkhard Morgenstern ◽  
Ivo Feussner ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Data Sets ◽  
Omics Data ◽  
Pathway Enrichment

The model organism as a system: integrating 'omics' data sets

2006 ◽  
Vol 7 (3) ◽  
pp. 198-210 ◽  
Author(s):  
Andrew R. Joyce ◽  
Bernhard Ø. Palsson
Keyword(s):  
Model Organism ◽  
Data Sets ◽  
Omics Data

scholarly journals Chromatic: WebAssembly-Based Cancer Genome Viewer

2018 ◽  
Vol 17 ◽  
pp. 117693511877197 ◽  
Author(s):  
Richard Finney ◽  
Daoud Meerzaman
Keyword(s):  
Ease Of Use ◽  
Data Sets ◽  
Web Browsers ◽  
Web Browser ◽  
Cancer Data ◽  
Genomic Variations ◽  
Genome Viewer ◽  
Code Format ◽  
Generation Sequencing ◽  
Cancer Bioinformatics

Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics.

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