Automated Author Profile

Quan, Jie

Current S-Index

5.9

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

12

Total datasets for this author

Average FAIR Score

13.8%

Average FAIR Score per dataset

Total Citations

10

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

MOESM5 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 5. Supplemental Material Primer Sequences. A list of all oligo-nucleotides employed in this study, using sequence conventions as outlined by IDT, Inc.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions13% FAIR0.5 Dataset Index
10.6084/m9.figshare.11665455.v1January 2020

MOESM5 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 5. Supplemental Material Primer Sequences. A list of all oligo-nucleotides employed in this study, using sequence conventions as outlined by IDT, Inc.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions15% FAIR0.5 Dataset Index
10.6084/m9.figshare.11665455January 2020

MOESM2 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 2: Table S1. A per-sample overview of sequencing metric details that were used to construct Table 1 of the main manuscript.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.11665428.v1January 2020

MOESM3 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 3: Table S2. A Gene Ontology analysis of the pathways ranked by p-value represented by the DEGs detected by 3’Pool-seq in the Wild-Type vs. GFAP-IL6 mouse model.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions13% FAIR0.5 Dataset Index
10.6084/m9.figshare.11665437.v1January 2020

MOESM4 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 4: Table S3. A Gene Ontology analysis of the pathways ranked by p-value represented by the DEGs detected by TruSeq in the Wild-Type vs. GFAP-IL6 mouse model.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions13% FAIR0.5 Dataset Index
10.6084/m9.figshare.11665446.v1January 2020

MOESM2 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 2: Table S1. A per-sample overview of sequencing metric details that were used to construct Table 1 of the main manuscript.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
0 Citations0 Mentions13% FAIR0.1 Dataset Index
10.6084/m9.figshare.11665428January 2020

MOESM3 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 3: Table S2. A Gene Ontology analysis of the pathways ranked by p-value represented by the DEGs detected by 3’Pool-seq in the Wild-Type vs. GFAP-IL6 mouse model.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
1 Citation0 Mentions13% FAIR0.5 Dataset Index
10.6084/m9.figshare.11665437January 2020

MOESM4 of 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Additional file 4: Table S3. A Gene Ontology analysis of the pathways ranked by p-value represented by the DEGs detected by TruSeq in the Wild-Type vs. GFAP-IL6 mouse model.

Authors

  • Sholder, Gabriel ;
  • Lanz, Thomas ;
  • Moccia, Robert ;
  • Quan, Jie ;
  • Aparicio-Prat, Estel ;
  • Stanton, Robert ;
  • Hualin Xi
0 Citations0 Mentions15% FAIR0.2 Dataset Index
10.6084/m9.figshare.11665446January 2020

Additional file 1: of Inhibition of 2-AG hydrolysis differentially regulates blood brain barrier permeability after injury

Table S1. Differentially regulated genes. Differentially regulated genes (increased or decreased by at least twofold) and p values for LPS-vehicle vs sham-vehicle and LPS-CPD-4645 vs LPS-vehicle groups. (XLSX 294 kb)

Authors

  • Piro, Justin ;
  • Suidan, Georgette ;
  • Quan, Jie ;
  • YeQing Pi ;
  • OâNeill, Sharon ;
  • Ilardi, Marissa ;
  • Pozdnyakov, Nikolay ;
  • Lanz, Thomas ;
  • Hualin Xi ;
  • Bell, Robert ;
  • Samad, Tarek
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.6268754.v1January 2018

Additional file 2: of Inhibition of 2-AG hydrolysis differentially regulates blood brain barrier permeability after injury

Table S2. Gene ontology analysis. Gene ontology analysis was performed using the DAVID bioinformatics tools. The enriched functional annotation clusters are listed along with p values for LPS vs Sham upregulated genes, LPS vs Sham downregulated genes, and CPD-4645 vs vehicle downregulated genes. (XLSX 157 kb)

Authors

  • Piro, Justin ;
  • Suidan, Georgette ;
  • Quan, Jie ;
  • YeQing Pi ;
  • OâNeill, Sharon ;
  • Ilardi, Marissa ;
  • Pozdnyakov, Nikolay ;
  • Lanz, Thomas ;
  • Hualin Xi ;
  • Bell, Robert ;
  • Samad, Tarek
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.6268775January 2018