IgGeneUsage: differential gene usage in immune repertoires

2020 ◽  
Vol 36 (11) ◽  
pp. 3590-3591 ◽  
Author(s):  
Simo Kitanovski ◽  
Daniel Hoffmann
Keyword(s):  
High Throughput Sequencing ◽  
Current Practice ◽  
Adaptive Immune Response ◽  
R Package ◽  
Supplementary Information ◽  
Adaptive Immune ◽  
Quantitative Property ◽  
Gene Usage ◽  
Differential Gene ◽  
Common Problems

Abstract Summary Decoding the properties of immune repertoires is key to understanding the adaptive immune response to challenges such as viral infection. One important quantitative property is differential usage of Ig genes between biological conditions. Yet, most analyses for differential Ig gene usage are performed qualitatively or with inadequate statistical methods. Here we introduce IgGeneUsage, a computational tool for the analysis of differential Ig gene usage. IgGeneUsage employs Bayesian inference with hierarchical models to analyze complex gene usage data from high-throughput sequencing experiments of immune repertoires. It quantifies differential Ig gene usage probabilistically and avoids some common problems related to the current practice of null-hypothesis significance testing. Availability and implementation IgGeneUsage is an R-package freely available as part of Bioconductor at: https://bioconductor.org/packages/IgGeneUsage/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals hypeR: An R Package for Geneset Enrichment Workflows

2019 ◽  
Author(s):  
Anthony Federico ◽  
Stefano Monti
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Wide Audience ◽  
Popular Method ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
One Stop ◽  
Recent Version

ABSTRACTSummaryGeneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.Availability and implementationThe most recent version of the package is available at https://github.com/montilab/hypeR.Supplementary informationComprehensive documentation and tutorials, are available at https://montilab.github.io/hypeR-docs.

scholarly journals TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Michael G. Schimek ◽  
Eva Budinská ◽  
Karl G. Kugler ◽  
Vendula Švendová ◽  
Jie Ding ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Independent Data ◽  
Rna Seq ◽  
Data Format ◽  
Valuable Data ◽  
Enormous Amount ◽  
Differential Gene ◽  
Technological Platforms

AbstractHigh-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format – rankings – for the same set of items. Here we present the

scholarly journals Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences

2019 ◽  
Vol 36 (6) ◽  
pp. 1731-1739 ◽  
Author(s):  
Erand Smakaj ◽  
Lmar Babrak ◽  
Mats Ohlin ◽  
Mikhail Shugay ◽  
Bryan Briney ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Adaptive Immune System ◽  
Average Frequency ◽  
Supplementary Information ◽  
Informed Decision Making ◽  
Bioinformatics Tool ◽  
Drug Candidates ◽  
Adaptive Immune ◽  
Human Genes ◽  
Sequencing Platforms

Abstract Summary Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics. There are currently multiple tools available for the annotation of antibody sequences. All downstream analyses such as choosing lead drug candidates depend on the correct annotation of these sequences; however, a thorough comparison of the performance of these tools has not been investigated. Here, we benchmark the performance of commonly used immunoinformatic tools, i.e. IMGT/HighV-QUEST, IgBLAST and MiXCR, in terms of reproducibility of annotation output, accuracy and speed using simulated and experimental high-throughput sequencing datasets. We analyzed changes in IMGT reference germline database in the last 10 years in order to assess the reproducibility of the annotation output. We found that only 73/183 (40%) V, D and J human genes were shared between the reference germline sets used by the tools. We found that the annotation results differed between tools. In terms of alignment accuracy, MiXCR had the highest average frequency of gene mishits, 0.02 mishit frequency and IgBLAST the lowest, 0.004 mishit frequency. Reproducibility in the output of complementarity determining three regions (CDR3 amino acids) ranged from 4.3% to 77.6% with preprocessed data. In addition, run time of the tools was assessed: MiXCR was the fastest tool for number of sequences processed per unit of time. These results indicate that immunoinformatic analyses greatly depend on the choice of bioinformatics tool. Our results support informed decision-making to immunoinformaticians based on repertoire composition and sequencing platforms. Availability and implementation All tools utilized in the paper are free for academic use. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals NetBoxR: Automated Discovery of Biological Process Modules by Network Analysis in R

2020 ◽  
Author(s):  
Eric Minwei Liu ◽  
Augustin Luna ◽  
Guanlan Dong ◽  
Chris Sander
Keyword(s):  
Cell Biology ◽  
Large Scale ◽  
Clustering Algorithm ◽  
High Throughput Sequencing ◽  
R Package ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Network Clustering ◽  
Data Types

AbstractSummaryLarge-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have accumulated a variety of high throughput sequencing and molecular profiling data, but it is still challenging to identify potentially causal genetic mutations in cancer as well as in other diseases in an automated fashion. We developed the NetBoxR package written in the R programming language, that makes use of the NetBox algorithm to identify candidate cancer-related processes. The algorithm makes use of a networkbased approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of functionally curated gene sets. A key aspect of this approach is its ability to combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology.Availability and implementationThe NetBoxR package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/[email protected]; [email protected]; [email protected] informationNone

scholarly journals lncRNAs in T lymphocytes: RNA-regulation at the heart of the immune response

2020 ◽  
Author(s):  
Leah M. Plasek ◽  
Saba Valadkhan
Keyword(s):  
Immune Response ◽  
Expression Pattern ◽  
Immune Cells ◽  
High Throughput Sequencing ◽  
Expression Patterns ◽  
Adaptive Immune Response ◽  
Small Subset ◽  
Specific Expression ◽  
Adaptive Immune

Genome-wide analyses in the last decade have uncovered the presence of a large number of long non-protein-coding transcripts which show highly tissue- and state-specific expression patterns. High throughput sequencing analyses in diverse subsets of immune cells have revealed a complex and dynamic expression pattern for these long non-coding RNAs (lncRNAs) which correlate with the functional states of immune cells. While the vast majority of lncRNAs expressed in immune cells remain unstudied, functional studies performed on a small subset have indicated that their state-specific expression pattern frequently has a regulatory impact on the function of immune cells. In vivo and in vitro studies have pointed to the involvement of lncRNAs in a wide variety of cellular processes including both the innate and adaptive immune response through mechanisms ranging from epigenetic and transcriptional regulation to sequestration of functional molecules in subcellular compartments. This review will mainly focus on the role of lncRNAs in CD4+ and CD8+ T cells, which play pivotal roles in adaptive immunity. While lncRNAs play important physiological roles in lymphocytic response to antigenic stimulation, differentiation into effector cells and secretion of cytokines, their dysregulated expression can promote or sustain pathological states such as autoimmunity, chronic inflammation, cancer and viremia. This, together with their highly cell type-specific expression patterns, makes lncRNAs ideal therapeutic targets and underscores the need for additional studies into the role of these understudied transcripts in adaptive immune response.

Is Alzheimer disease a failure of mobilizing immune defense? Lessons from cognitively fit oldest-old

2019 ◽  
Vol 21 (1) ◽  
pp. 7-19 ◽  
Keyword(s):  
Alzheimer Disease ◽  
Cognitive Aging ◽  
Oldest Old ◽  
Adaptive Immune Response ◽  
Effector T Cells ◽  
Immune Defense ◽  
Supporting Evidence ◽  
Therapeutic Approaches ◽  
Related Data ◽  
Adaptive Immune

Multifaceted evidence supports the hypothesis that inflammatory-immune mechanisms contribute to Alzheimer disease (AD) neuropathology and genetic association of several immune specific genes (TREM2, CR1, and CD33) suggests that maladaptive immune responses may be pivotal drivers of AD pathogenesis. We reviewed microglia-related data from postmortem AD studies and examined supporting evidence from AD animal models to answer the following questions: i) What is the temporal sequence of immune activation in AD progression and what is its impact on cognition? ii) Are there discordant, "primed", microglia responses in AD vs successful cognitive aging? iii) Does central nervous system (CNS) repair in aging depend on recruitment of the elements of cellular adaptive immune response such as effector T cells, and can the recruitment of systemic immune cells ameliorate AD neuropathology? iv) How effective are the immune-system-based therapeutic approaches currently employed for the treatment of AD?

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