In Silico HLA Typing Using Standard RNA-Seq Sequence Reads

2015 ◽  
pp. 247-258 ◽  
Author(s):  
Sebastian Boegel ◽  
Jelle Scholtalbers ◽  
Martin Löwer ◽  
Ugur Sahin ◽  
John C. Castle
Keyword(s):  
In Silico ◽  
Hla Typing ◽  
Rna Seq

scholarly journals Identifying new COVID-19 receptor Neuropilin-1 in severe Alzheimer’s diseases patients group brain using genome-wide association study approach

2021 ◽  
Author(s):  
Key-Hwan Lim ◽  
Sumin Yang ◽  
Sung-Hyun Kim ◽  
Jae-Yeol Joo
Keyword(s):  
In Silico ◽  
Genome Wide Association Study ◽  
Transmembrane Protein ◽  
In Silico Analysis ◽  
Therapeutic Drug ◽  
Rna Seq ◽  
Total Rna ◽  
Neuropilin 1 ◽  
Silico Analysis ◽  
Brain Tissues

Abstract Background Numerous studies have been conducted on different aspects of the COVID-19 (coronavirus disease 2019) pandemic, which is caused by SARS-CoV-2, since its emergence in late 2019. Mutual relations among SARS-CoV-2 and neuro-pathophysiological phenomena are continuously being demonstrated, and several underlying diseases, such as those in the elderly, are positively correlated with susceptibility to SARS-CoV-2 infection. The expression of angiotensin converting enzyme 2 (ACE2), which is required for SARS-CoV-2 infection, was recently demonstrated to be increased in Alzheimer’s disease (AD) patients. Methods Recent preclinical studies have shown that Neuropilin-1 (NRP1), which is a transmembrane protein with roles in neuronal development, axonal outgrowth, and angiogenesis, also plays a role in the infectivity of SARS-CoV-2. Thus, we hypothesized that NRP1 may be upregulated in AD patients and that a correlation between AD and SARS-CoV-2 NRP1-mediated infectivity may exist. We used an AD mouse model that mimics AD and performed high throughput total RNA-seq with brain tissue and whole blood. For quantification of NPR1 in AD, brain tissues and blood were subjected to western blotting and RT-qPCR analysis. In silico analysis for NRP1 expression in AD patients has been performed on the human hippocampus data sets (GSE4226, GSE1297). Results Many cases of severe symptom of COVID-19 are concentrated in elderly group who have complications such as diabetes, degenerative disease, and brain disorders. Total RNA-seq analysis showed that Nrp1 gene was commonly overexpressed in AD model. Similar to ACE2, NRP1 protein also strongly expressed in the AD brain tissues. Interestingly, in silico analysis revealed that the level of expression for NRP1 was distinct at age and AD progression. Conclusions Given that the NRP1 is highly expressed in AD, it will be important to understand and predict that NRP1 may a risk factor for SARS-CoV-2 infection in AD patients. This will support to development of potential therapeutic drug to reduce SARS-CoV-2 transmission.

scholarly journals In Silico and In Vitro Analysis of lncRNA XIST Reveals a Panel of Possible Lung Cancer Regulators and a Five-Gene Diagnostic Signature

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3499
Author(s):  
Periklis Katopodis ◽  
Qiduo Dong ◽  
Heerni Halai ◽  
Cristian I. Fratila ◽  
Andreas Polychronis ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
In Silico ◽  
Cell Biology ◽  
Tumour Progression ◽  
Cell Communication ◽  
Nucleic Acid Metabolism ◽  
Rna Seq ◽  
Diagnostic Potential

Long non-coding RNAs (lncRNAs) perform a wide functional repertoire of roles in cell biology, ranging from RNA editing to gene regulation, as well as tumour genesis and tumour progression. The lncRNA X-inactive specific transcript (XIST) is involved in the aetiopathogenesis of non-small cell lung cancer (NSCLC). However, its role at the molecular level is not fully elucidated. The expression of XIST and co-regulated genes TSIX, hnRNPu, Bcl-2, and BRCA1 analyses in lung cancer (LC) and controls were performed in silico. Differentially expressed genes (DEGs) were determined using RNA-seq in H1975 and A549 NSCLC cell lines following siRNA for XIST. XIST exhibited sexual dimorphism, being up-regulated in females compared to males in both control and LC patient cohorts. RNA-seq revealed 944 and 751 DEGs for A549 and H1975 cell lines, respectively. These DEGs are involved in signal transduction, cell communication, energy pathways, and nucleic acid metabolism. XIST expression associated with TSIX, hnRNPu, Bcl-2, and BRCA1 provided a strong collective feature to discriminate between controls and LC, implying a diagnostic potential. There is a much more complex role for XIST in lung cancer. Further studies should concentrate on sex-specific changes and investigate the signalling pathways of the DEGs following silencing of this lncRNA.

Validation of an expanded neoantigen identification platform for therapeutic and diagnostic use in immuno-oncology.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11589-11589 ◽  
Author(s):  
Sean Michael Boyle ◽  
Jason Harris ◽  
Gabor Bartha ◽  
Ravi Alla ◽  
Patrick Jongeneel ◽  
...  
Keyword(s):  
In Silico ◽  
Cancer Vaccines ◽  
Clinical Applications ◽  
Ffpe Tissue ◽  
Hla Typing ◽  
Clinical Settings ◽  
Analytical Validation ◽  
Diagnostic Use ◽  
Complex Processes ◽  
Personalized Cancer

11589 Background: Neoantigen identification is increasingly critical for clinical immuno-oncology applications including predicting immunotherapy response and neoantigen-based personalized cancer vaccines. Although standard research pipelines have been developed to aid neoantigen identification, building a robust, validated neoantigen identification platform suitable for clinical applications has been challenging due to the complex processes involved. Methods: To improve neoantigen identification, we extended standard sequencing and informatics methods. We developed an augmented and content enhanced (ACE) exome sequenced at 200X to increase sensitivity to SNPs and indels used for neoantigen identification as well as HLA performance. To accurately identify fusions and variants from RNA, we optimized our ACE transcriptome for FFPE tissue. To improve neoantigen pipelines based on MHC binding algorithms, we developed peptide phasing, high accuracy HLA typing, TCR interaction predictors, and transcript isoform estimation tools to detect neoantigens from indel and fusion events. We performed comprehensive analytical validation of the platform including the ACE Exome, somatic SNV/indel calls, RNA based variant and fusion calls, and HLA typing. This was followed by an overall in silico validation of neoantigen identification using 23 experimentally validated immunogenic neoepitopes spiked into exome data. Results: Analytical validation of our ACE exome platform showed > 97% sensitivity for small variants with a specificity of > 98% at minor allele frequency > 10%. From the ACE transcriptome we achieved a fusion sensitivity of > 99% and RNA based variant calls sensitivity of > 97%. Our ACE exome based HLA typing was 98% and 95% concordant with Class I and II HLA results (respectively) from clinical testing. Our in silico validation of neoantigen predictions resulted in identification of 22 out of 23 immunogenic neoepitopes. Conclusions: We developed sequencing and informatics improvements to standard approaches that can enhance neoantigen identification and demonstrated a comprehensive validation approach that may support neoantigen use in future clinical settings.

STEM-24. INTERFERON SIGNALING REGULATES THE PROLIFERATION AND MESENCHYMAL PHENOTYPE OF GLIOBLASTOMA STEM CELLS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi26-vi26
Author(s):  
Sabbir Khan ◽  
Rajasekaran Mahalingam ◽  
Shayak Sen ◽  
Kaitlin Gandy ◽  
Kristin Alfaro-Munoz ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Single Cell ◽  
In Silico ◽  
The Cancer Genome Atlas ◽  
Cytokine Signaling ◽  
Rna Seq ◽  
Mesenchymal Phenotype ◽  
Cancer Genome Atlas ◽  
Stat1 Signaling

Abstract Interferon (IFN) signaling contributes to stemness, cell proliferation, cell death, and cytokine signaling in cancer and immune cells; however, the role of IFN signaling in glioblastoma (GBM) and GBM stem-like cells (GSCs) is unclear. This study aimed to investigate the cancer cell-intrinsic IFN signaling in tumorigenesis and malignant phenotype of GBM. We characterized cell-intrinsic IFN signaling in The Cancer Genome Atlas, patient-derived cohorts of GSCs, and published single-cell RNA sequencing datasets by in-silico analyses. The in-silico findings were further validated by evaluating the cytokine secretion and using pharmacological activators and blockers of IFN/transducer and activator of transcription 1 (STAT1) signaling. We found that GSCs and GBM tumors exhibited differential cell-intrinsic IFN signaling, and high IFN/STAT1 signaling is associated with mesenchymal phenotype and poor survival outcomes. Ruxolitinib, a pharmacological inhibitor of IFN/STAT1, abolished the IFN/STAT1 signaling in GSCs with intrinsically high IFN signaling. IFN-γ treatment for 1 week promotes the mesenchymal phenotype in GSCs with low IFN signature. In addition, chronic inhibition of IFN/STAT1 signaling with ruxolitinib decreased cell proliferation and mesenchymal signatures (CD44, YKL40, and TIMP1) in GSCs with intrinsically active IFN/STAT1 signaling. Publicly available human glioma single-cell RNA-seq (scRNA-seq) datasets analyses showed that both tumor and nontumor cells expressed IFN signaling genes, and the mesenchymal signature was highly expressed in the same cluster where IFN signaling genes were upregulated. We demonstrated that cell-intrinsic IFN signaling in GSCs and GBM tumors is associated with mesenchymal signatures and cell proliferation. Our study provides evidence for the possibility of targeting IFN signaling in a specific group of GBM patients.

168-P: In silico resolution of ambiguous HLA typing data

2008 ◽  
Vol 69 ◽  
pp. S91 ◽  
Author(s):  
J. Listgarten ◽  
Z. Brumme ◽  
C. Kadie ◽  
G. Xiaojiang ◽  
B. Walker ◽  
...  
Keyword(s):  
In Silico ◽  
Hla Typing

scholarly journals Robustness and applicability of functional genomics tools on scRNA-seq data

2019 ◽  
Author(s):  
Christian H. Holland ◽  
Jovan Tanevski ◽  
Jan Gleixner ◽  
Manu P. Kumar ◽  
Elisabetta Mereu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Real Data ◽  
Drop Out ◽  
Rna Seq ◽  
Knock Out ◽  
Genomics Tools

AbstractMany tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.

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