Supplementary data for: High-Throughput Transcriptome Sequencing for SNP and Gene Discovery in a Moth

2012 ◽  
Vol 41 (4) ◽  
Author(s):  
Nicholas Miller
Keyword(s):  
High Throughput ◽  
Transcriptome Sequencing ◽  
Gene Discovery ◽  
Supplementary Data

scholarly journals High-Throughput Transcriptome Sequencing for Snp and Gene Discovery in a Moth

2012 ◽  
Vol 41 (4) ◽  
pp. 997-1007 ◽  
Author(s):  
Nicholas J. Miller ◽  
Jing Sun ◽  
Thomas W. Sappington
Keyword(s):  
High Throughput ◽  
Transcriptome Sequencing ◽  
Gene Discovery

scholarly journals Organoid Models for Cancer Research

2019 ◽  
Vol 3 (1) ◽  
pp. 223-234 ◽  
Author(s):  
Hans Clevers ◽  
David A. Tuveson
Keyword(s):  
Cancer Research ◽  
Tumor Microenvironment ◽  
Personalized Medicine ◽  
High Throughput ◽  
Stromal Cells ◽  
Transcriptome Sequencing ◽  
Cancer Biology ◽  
Three Dimensional ◽  
Model Systems ◽  
Liver Cancers

Organoid cultures have emerged as powerful model systems accelerating discoveries in cellular and cancer biology. These three-dimensional cultures are amenable to diverse techniques, including high-throughput genome and transcriptome sequencing, as well as genetic and biochemical perturbation, making these models well suited to answer a variety of questions. Recently, organoids have been generated from diverse human cancers, including breast, colon, pancreas, prostate, bladder, and liver cancers, and studies involving these models are expanding our knowledge of the etiology and characteristics of these malignancies. Co-cultures of cancer organoids with non-neoplastic stromal cells enable investigation of the tumor microenvironment. In addition, recent studies have established that organoids have a place in personalized medicine approaches. Here, we describe the application of organoid technology to cancer discovery and treatment.

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers

2020 ◽  
Author(s):  
Yuansheng Liu ◽  
Xiaocai Zhang ◽  
Quan Zou ◽  
Xiangxiang Zeng
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complementary Strand ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Computational Resources

Abstract Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Gene discovery and annotation using LCM-454 transcriptome sequencing

2006 ◽  
Vol 17 (1) ◽  
pp. 69-73 ◽  
Author(s):  
S. J. Emrich ◽  
W. B. Barbazuk ◽  
L. Li ◽  
P. S. Schnable
Keyword(s):  
Transcriptome Sequencing ◽  
Gene Discovery ◽  
454 Transcriptome Sequencing

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

2020 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Dandan Wang ◽  
Shixue Sun ◽  
Heping Zhang
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Theoretical Basis ◽  
Sampling Error ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automation Technology ◽  
General Public License

Abstract Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals High-throughput transcriptome sequencing of the cold seep mussel Bathymodiolus platifrons

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Yue Him Wong ◽  
Jin Sun ◽  
Li Sheng He ◽  
Lian Guo Chen ◽  
Jian-Wen Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Transcriptome Sequencing ◽  
Cold Seep
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