scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2019 ◽  
Author(s):  
Ana Carolina Coelho ◽  
Sandro de Souza
Keyword(s):  
Immune Cells ◽  
Somatic Mutations ◽  
High Sensitivity ◽  
High Rate ◽  
Graphical Interface ◽  
Rna Seq ◽  
Automated Pipeline ◽  
User Friendly ◽  
Ngs Data ◽  
Multiple Samples

Abstract Background Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.Results We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from NGS data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in the TCGA melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.Conclusion neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available on Github at https://github.com/neoanthill/neoANT-HILL.

scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2020 ◽  
Author(s):  
Ana Carolina Coelho ◽  
Andre Fonseca ◽  
Danilo Martins ◽  
Paulo Lins ◽  
Lucas da Cunha ◽  
...  
Keyword(s):  
High Sensitivity ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Graphical Interface ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
Automated Pipeline ◽  
User Friendly ◽  
Ngs Data ◽  
Multiple Samples

Abstract Background Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in the The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available on github at https://github.com/neoanthill/neoANT-HILL .

scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2020 ◽  
Author(s):  
Ana Carolina Coelho ◽  
Andre Fonseca ◽  
Danilo Martins ◽  
Paulo Lins ◽  
Lucas da Cunha ◽  
...  
Keyword(s):  
High Sensitivity ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Graphical Interface ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
Automated Pipeline ◽  
User Friendly ◽  
Ngs Data ◽  
Multiple Samples

Abstract Background Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.Results We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in the The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.Conclusion neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available on github at https://github.com/neoanthill/neoANT-HILL .

scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2020 ◽  
Author(s):  
Ana Carolina Coelho ◽  
Andre Fonseca ◽  
Danilo Martins ◽  
Paulo Lins ◽  
Lucas da Cunha ◽  
...  
Keyword(s):  
High Sensitivity ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Graphical Interface ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
Automated Pipeline ◽  
User Friendly ◽  
Ngs Data ◽  
Multiple Samples

Abstract Background: Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results: We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.Conclusion: neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.

scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2020 ◽  
Author(s):  
Ana Carolina Coelho ◽  
Andre Fonseca ◽  
Danilo Martins ◽  
Paulo Lins ◽  
Lucas da Cunha ◽  
...  
Keyword(s):  
High Sensitivity ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Graphical Interface ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
Automated Pipeline ◽  
User Friendly ◽  
Ngs Data ◽  
Multiple Samples

Abstract Background Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in the The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available on github at https://github.com/neoanthill/neoANT-HILL .

scholarly journals Easymap: A User-Friendly Software Package for Rapid Mapping-by-Sequencing of Point Mutations and Large Insertions

2021 ◽  
Vol 12 ◽  
Author(s):  
Samuel Daniel Lup ◽  
David Wilson-Sánchez ◽  
Sergio Andreu-Sánchez ◽  
José Luis Micol
Keyword(s):  
Point Mutations ◽  
Rapid Identification ◽  
Graphical Interface ◽  
Rna Seq ◽  
Source Program ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Mapping By Sequencing ◽  
Generation Sequencing

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

scholarly journals neoANT-HILL: an integrated tool for identification of potential neoantigens

2019 ◽  
Author(s):  
Ana Carolina M F Coelho ◽  
André L Fonseca ◽  
Danilo L Martins ◽  
Lucas M da Cunha ◽  
Paulo B R Lins ◽  
...  
Keyword(s):  
Immune Responses ◽  
False Positive ◽  
Somatic Mutations ◽  
Cancer Genome ◽  
Current Work ◽  
Rna Seq ◽  
Link Type ◽  
Technological Advances ◽  
User Friendly ◽  
Ngs Data

AbstractCancer neoantigens have attracted great interest in immunotherapy due to their ability to elicit antitumoral immune responses. These antigens are formed due to somatic mutations in the cancer genome that result in alterations of the original protein. Although current technological advances in neoantigen identification, it remains a challenging and a large number of false-positive continue to exist. In the current work, we present neoANT-HILL, an automatized user-friendly tool that integrates several immunogenomic analysis to improve neoantigens detection from NGS data. The program input can be a file with somatic mutations called and/or RNA-seq data. Our tool was applied on somatic mutations of melanoma dataset from TCGA and found that neoANT-HILL was able to predicted potential neoantigens. The software is available on github athttps://github.com/neoanthill/neoANT-HILL.

scholarly journals TACITuS: transcriptomic data collector, integrator, and selector on big data platform

2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Salvatore Alaimo ◽  
Antonio Di Maria ◽  
Dennis Shasha ◽  
Alfredo Ferro ◽  
Alfredo Pulvirenti
Keyword(s):  
High Throughput ◽  
Rna Seq ◽  
Web Based ◽  
Large Bandwidth ◽  
Specific Analysis ◽  
Galaxy Environment ◽  
Public Repositories ◽  
Microarray Datasets ◽  
User Friendly ◽  
Ngs Data

Abstract Background Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. Results TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. Conclusions TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

scholarly journals ProkSeq for complete analysis of RNA-Seq data from prokaryotes

2020 ◽  
Author(s):  
A K M Firoj Mahmud ◽  
Nicolas Delhomme ◽  
Soumyadeep Nandi ◽  
Maria Fällman
Keyword(s):  
Gene Expression ◽  
Pathogenic Bacteria ◽  
Supplementary Information ◽  
Complete Analysis ◽  
Rna Seq ◽  
Differential Gene ◽  
User Friendly ◽  
Multiple Samples ◽  
Data Analysis Pipeline

Abstract Summary Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for studying gene expression, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results. Availability and implementation ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. The code is available on Github: https://github.com/snandiDS/prokseq and a detailed user documentation, including a manual and tutorial can be found at https://prokseqV20.readthedocs.io Supplementary information Supplementary data are available at Bioinformatics online

scholarly journals ProkSeq for complete analysis of RNA-seq data from prokaryotes

2020 ◽  
Author(s):  
A K M Firoj Mahmud ◽  
Soumyadeep Nandi ◽  
Maria Fällman
Keyword(s):  
Gene Expression ◽  
Pathogenic Bacteria ◽  
Complete Analysis ◽  
Rna Seq ◽  
Link Type ◽  
Eukaryotic Genes ◽  
Differential Gene ◽  
User Friendly ◽  
Multiple Samples

AbstractSummarySince its introduction, RNA-seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, the current tools for assessing gene expression have been designed around the structures of eukaryotic genes. There are a few stand-alone tools designed for prokaryotes, and they require improvement. A well-defined pipeline for prokaryotes that includes all the necessary tools for quality control, determination of differential gene expression, downstream pathway analysis, and normalization of data collected in extreme biological conditions is still lacking. Here we describe ProkSeq, a user-friendly, fully automated RNA-seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data, and visualizing data and results, and it produces publication-quality figures.Availability and implementationProkSeq is implemented in Python and is published under the ISC open source license. The tool and a detailed user manual are hosted at Docker: https://hub.docker.com/repository/docker/snandids/prokseq-v2.1, Anaconda: https://anaconda.org/snandiDS/prokseq; Github: https://github.com/snandiDS/prokseq.

scholarly journals Easymap: a user-friendly software package for rapid mapping by sequencing of point mutations and large insertions

2021 ◽  
Author(s):  
Samuel Daniel Lup ◽  
David Wilson-Sánchez ◽  
Sergio Andreu-Sánchez ◽  
José Luis Micol
Keyword(s):  
Point Mutations ◽  
Rapid Identification ◽  
Rna Seq ◽  
Source Program ◽  
Ethylmethane Sulfonate ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Mapping By Sequencing ◽  
Generation Sequencing

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest in order to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existent software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethylmethane sulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results.

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