Application of High-Throughput Sequencing Data Mining in Comparison of Gene Expression Profile in Renal Cell Carcinoma and Normal Renal Cell by RNA-Seq

2020 ◽  
pp. 359-365
Author(s):  
Yunhai Yu ◽  
Hongmei Xu ◽  
Shaoning Guo ◽  
Na Wang
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
High Throughput ◽  
Renal Cell ◽  
High Throughput Sequencing ◽  
Rna Seq ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

scholarly journals HTSeq - A Python framework to work with high-throughput sequencing data

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Standard Work ◽  
Data Formats ◽  
High Throughput Sequencing Data ◽  
Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

scholarly journals Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

2015 ◽  
Author(s):  
Ben Busby ◽  
Allissa Dillman ◽  
Claire L. Simpson ◽  
Ian Fingerman ◽  
Sijung Yun ◽  
...  
Keyword(s):  
Web Service ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Genomic Analysis ◽  
National Institutes Of Health ◽  
Rna Seq ◽  
Sequencing Data ◽  
Collaborative Software ◽  
High Throughput Sequencing Data ◽  
Collaborative Software Development

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

Generate gene expression profile from high-throughput sequencing data

2011 ◽  
Vol 6 (6) ◽  
pp. 1131-1145
Author(s):  
Hui Liu ◽  
Zhichao Jiang ◽  
Xiangzhong Fang ◽  
Hanjiang Fu ◽  
Xiaofei Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

The simple fool's guide to population genomics via RNA ‐Seq: an introduction to high‐throughput sequencing data analysis

2012 ◽  
Vol 12 (6) ◽  
pp. 1058-1067 ◽  
Author(s):  
Pierre Wit ◽  
Melissa H. Pespeni ◽  
Jason T. Ladner ◽  
Daniel J. Barshis ◽  
François Seneca ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Population Genomics ◽  
Rna Seq ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis

scholarly journals Clinicopathologic features of TDO2 overexpression in renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Quoc Thang Pham ◽  
Daiki Taniyama ◽  
Yohei Sekino ◽  
Shintaro Akabane ◽  
Takashi Babasaki ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
In Silico Analysis ◽  
Immunohistochemical Analysis ◽  
Rna Seq ◽  
Anoikis Resistance ◽  
Silico Analysis ◽  
Proliferation And Invasion

Abstract Background Tryptophan 2,3-dioxygenase (TDO2) is the primary enzyme catabolizing tryptophan. Several lines of evidence revealed that overexpression of TDO2 is involved in anoikis resistance, spheroid formation, proliferation, and invasion and correlates with poor prognosis in some cancers. The aim of this research was to uncover the expression and biofunction of TDO2 in renal cell carcinoma (RCC). Methods To show the expression of TDO2 in RCC, we performed qRT-PCR and immunohistochemistry in integration with TCGA data analysis. The interaction of TDO2 with PD-L1, CD44, PTEN, and TDO2 expression was evaluated. We explored proliferation, colony formation, and invasion in RCC cells line affected by knockdown of TDO2. Results RNA-Seq and immunohistochemical analysis showed that TDO2 expression was upregulated in RCC tissues and was associated with advanced disease and poor survival of RCC patients. Furthermore, TDO2 was co-expressed with PD-L1 and CD44. In silico analysis and in vitro knockout of PTEN in RCC cell lines revealed the ability of PTEN to regulate the expression of TDO2. Knockdown of TDO2 suppressed the proliferation and invasion of RCC cells. Conclusion Our results suggest that TDO2 might have an important role in disease progression and could be a promising marker for targeted therapy in RCC. (199 words)

Diagnostic Utility of RNA-Seq for Evaluation of PD-L1 Expression in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Maria Sorokina ◽  
Danil Stupichev ◽  
Yang Lyu ◽  
Akshaya Ramachandran ◽  
Natalia Miheecheva ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Diagnostic Utility ◽  
Rna Seq ◽  
Cell Renal Cell Carcinoma
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