scholarly journals Target variant detection in leukemia using unaligned RNA-Seq reads

2018 ◽  
Author(s):  
Eric Olivier Audemard ◽  
Patrick Gendron ◽  
Vincent-Philippe Lavallée ◽  
Josée Hébert ◽  
Guy Sauvageau ◽  
...  
Keyword(s):  
Variant Calling ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Read Mapping ◽  
Targeted Mutation ◽  
Cancer Genome Atlas ◽  
Computationally Intensive ◽  
And Performance ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

AbstractMutations identified in each Acute Myeloid Leukemia (AML) patients are useful for prognosis and to select targeted therapies. Detection of such mutations by the analysis of Next-Generation Sequencing (NGS) data requires a computationally intensive read mapping step and application of several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent AML cohorts (The Cancer Genome Atlas and Leucegene), to show that mutation detection bykmis fast, accurate and mainly limited by sequencing depth. Therefore,kmallows to establish fast diagnostics from NGS data, and could be suitable for clinical applications.

scholarly journals Targeted variant detection using unaligned RNA-Seq reads

2019 ◽  
Vol 2 (4) ◽  
pp. e201900336 ◽  
Author(s):  
Eric Olivier Audemard ◽  
Patrick Gendron ◽  
Albert Feghaly ◽  
Vincent-Philippe Lavallée ◽  
Josée Hébert ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
The Cancer Genome Atlas ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Targeted Mutation ◽  
Computationally Intensive ◽  
And Performance ◽  
Generation Sequencing

Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.

scholarly journals Detecting copy number alterations in RNA-Seq using SuperFreq

2020 ◽  
Author(s):  
Christoffer Flensburg ◽  
Alicia Oshlack ◽  
Ian J. Majewski
Keyword(s):  
Copy Number ◽  
Point Mutations ◽  
Variant Calling ◽  
Read Depth ◽  
The Cancer Genome Atlas ◽  
Nucleotide Polymorphisms ◽  
Rna Seq ◽  
Copy Number Alterations ◽  
Cancer Genome Atlas ◽  
High Level

AbstractCalling copy number alterations (CNAs) from RNA-Seq is challenging, because differences in gene expression mean that read depth across genes varies by several orders of magnitude and there is a paucity of informative single nucleotide polymorphisms (SNPs). We previously developed SuperFreq to analyse exome data of tumours by combining variant calling and copy number estimation in an integrated pipeline. Here we have used the SuperFreq framework for the analysis of RNA sequencing (RNA-Seq) data, which allows for the detection of absolute and allele sensitive CNAs. SuperFreq uses an error-propagation framework to combine and maximise the information available in the read depth and B-allele frequencies of SNPs (BAFs) to make CNA calls on RNA-seq data. We used data from The Cancer Genome Atlas (TCGA) to evaluate the CNA called from RNA-Seq with those generated from SNP-arrays. When ploidy estimates were consistent, we found excellent agreement with CNAs called from DNA of over 98% of the genome for acute myeloid leukaemia (TCGA-AML, n=116) and 87% for colorectal cancer (TCGA-CRC, n=377), which has a much higher CNA burden. As expected, the sensitivity of CNA calling from RNA-Seq was dependent on gene density. Nonetheless, using RNA-Seq SuperFreq detected 78% of CNA calls covering 100 or more genes with a precision of 94%. Recall dropped markedly for focal events, but this also depended on the signal intensity. For example, in the CRC cohort SuperFreq identified 100% (7/7) of cases with high-level amplification of ERBB2, where the copy number was typically >20, but identified only 6% (1/17) of cases with moderate amplification of IGF2, typically 4 or 5 copies over a smaller region (median 5 flanking genes for IGF2, compared to 20 for ERBB2). We were able to reproduce the relationship between mutational load and CNA profile in CRC using RNA-Seq alone. SuperFreq offers an integrated platform for identification of CNAs and point mutations from RNA-seq in cancer transcriptomes.The software is implemented in R and is available through GitHub: https://github.com/ChristofferFlensburg/SuperFreq.

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 Enabling cross-study analysis of RNA-Sequencing data

2017 ◽  
Author(s):  
Qingguo Wang ◽  
Joshua Armenia ◽  
Chao Zhang ◽  
Alexander V. Penson ◽  
Ed Reznik ◽  
...  
Keyword(s):  
Large Scale ◽  
Tissue Expression ◽  
Underlying Disease ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Whole Transcriptome Sequencing ◽  
Cancer Genome Atlas ◽  
Next Generation Sequencing Ngs ◽  
Different Sources

AbstractDriven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

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 PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

2018 ◽  
Vol 19 (10) ◽  
pp. 3250 ◽  
Author(s):  
Anna Sorrentino ◽  
Antonio Federico ◽  
Monica Rienzo ◽  
Patrizia Gazzerro ◽  
Maurizio Bifulco ◽  
...  
Keyword(s):  
Family Members ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Gene ◽  
Rna Seq ◽  
Set Domain ◽  
Primary Tumors ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein–protein, protein–RNA, or protein–DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

scholarly journals Visual Display of 5p-arm and 3p-arm miRNA Expression with a Mobile Application

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Yu Pan ◽  
Wei-Ting Kuo ◽  
Chien-Yuan Chiu ◽  
Wen-chang Lin
Keyword(s):  
Mirna Expression ◽  
Expression Patterns ◽  
Mirna Gene ◽  
Visual Display ◽  
Mature Mirnas ◽  
Mobile App ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
User Friendly

MicroRNAs (miRNAs) play important roles in human cancers. In previous studies, we have demonstrated that both 5p-arm and 3p-arm of mature miRNAs could be expressed from the same precursor and we further interrogated the 5p-arm and 3p-arm miRNA expression with a comprehensive arm feature annotation list. To assist biologists to visualize the differential 5p-arm and 3p-arm miRNA expression patterns, we utilized a user-friendly mobile App to display. The Cancer Genome Atlas (TCGA) miRNA-Seq expression information. We have collected over 4,500 miRNA-Seq datasets from 15 TCGA cancer types and further processed them with the 5p-arm and 3p-arm annotation analysis pipeline. In order to be displayed with the RNA-Seq Viewer App, annotated 5p-arm and 3p-arm miRNA expression information and miRNA gene loci information were converted into SQLite tables. In this distinct application, for any given miRNA gene, 5p-arm miRNA is illustrated on the top of chromosome ideogram and 3p-arm miRNA is illustrated on the bottom of chromosome ideogram. Users can then easily interrogate the differentially 5p-arm/3p-arm expressed miRNAs with their mobile devices. This study demonstrates the feasibility and utility of RNA-Seq Viewer App in addition to mRNA-Seq data visualization.

scholarly journals ERK1/2 signaling regulates the immune microenvironment and macrophage recruitment in glioblastoma

2019 ◽  
Vol 39 (9) ◽  
Author(s):  
Claire Lailler ◽  
Christophe Louandre ◽  
Mony Chenda Morisse ◽  
Thomas Lhossein ◽  
Corinne Godin ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Recent Observation ◽  
The Cancer Genome Atlas ◽  
Response To Treatment ◽  
Mrna Levels ◽  
Rna Seq ◽  
Immune Microenvironment ◽  
Oncogenic Signaling ◽  
Cancer Genome Atlas ◽  
Human Gbm

Abstract The tumor microenvironment is an important determinant of glioblastoma (GBM) progression and response to treatment. How oncogenic signaling in GBM cells modulates the composition of the tumor microenvironment and its activation is unclear. We aimed to explore the potential local immunoregulatory function of ERK1/2 signaling in GBM. Using proteomic and transcriptomic data (RNA seq) available for GBM tumors from The Cancer Genome Atlas (TCGA), we show that GBM with high levels of phosphorylated ERK1/2 have increased infiltration of tumor-associated macrophages (TAM) with a non-inflammatory M2 polarization. Using three human GBM cell lines in culture, we confirmed the existence of ERK1/2-dependent regulation of the production of the macrophage chemoattractant CCL2/MCP1. In contrast with this positive regulation of TAM recruitment, we found no evidence of a direct effect of ERK1/2 signaling on two other important aspects of TAM regulation by GBM cells: (1) the expression of the immune checkpoint ligands PD-L1 and PD-L2, expressed at high mRNA levels in GBM compared with other solid tumors; (2) the production of the tumor metabolite lactate recently reported to dampen tumor immunity by interacting with the receptor GPR65 present on the surface of TAM. Taken together, our observations suggest that ERK1/2 signaling regulates the recruitment of TAM in the GBM microenvironment. These findings highlight some potentially important particularities of the immune microenvironment in GBM and could provide an explanation for the recent observation that GBM with activated ERK1/2 signaling may respond better to anti-PD1 therapeutics.

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