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Transcript
BioXpress: an integrated RNA-seqderived gene expression database
for pan-cancer analysis
Presented by Yang Pan
Department of Biochemistry & Molecular Medicine
The George Washington University
Authors: Wan Q, Dingerdissen H, Fan Y, Gulzar N, Pan Y, Wu T-J, Yan C, Zhang H, Mazumder R. Database (2015).
Contents
• Motivation behind BioXpress
• Automatic biocuration (data collection and
unification pipeline)
• Manual biocuration (literature mining protocol)
• Pan-cancer analysis using BioXpress
• Ongoing works and future plans
CONTENT
Biocuration of gene expression data
• Several national and international projects are underway
that aim to capture and analyze the expression profiles of
thousands of tumors (ICGC, TCGA) and various tissues;
there are already thousands of publications that describe
over- and under-expression of specific genes in cancer.
• An integrated view of the expression profiles of the human
genes obtained from NGS technology such as RNA
sequencing (RNA-seq) is important for pan-cancer analysis.
• Better understand how data from publications from last
several decades on cancer-related gene expression match
with large-scale studies such as TCGA and ICGC
I. Motivation behind BioXpress
Data Sources
I. Motivation behind BioXpress
Automatic curation pipeline of BioXpress
DEseq normalization
II. Automatic biocuration of expression data
Search Page
Search by gene name/UniProtKB AC/RefSeq AC:
Search by cancer type:
II. Automatic biocuration of expression data
Search by Gene/Protein
II. Automatic biocuration of expression data
Search by Gene/Protein
Tumor Expression:
II. Automatic biocuration of expression data
Search by Gene/Protein
Baseline expression:
II. Automatic biocuration of expression data
Search by Cancer Types
II. Automatic biocuration of expression data
-Cancer Gene Census
(CGC)
-Significant Mutated
Genes (SMGs)
-Loss of Functional
sites caused by
somatic mutation
PubMed
searching and
reviewing
-Manual
-Semi-manual (NLP
algorithms
generates a list)
Step3
Generating a
prioritized gene
list
Step2
Step1
Manual curation protocol of BioXpress
Mapping to
unified cancer
terms and
inserting to
database
Details about Cancer
Disease Ontology
please refer to
Lightning Talks by Dr.
Raja Mazumder and
our poster
III. Manual curation protocol for literature mining
Manual curation protocol
General process:
1. Genes identified in our previous pan-cancer study were prioritized (Pan Y.
et al. (2014) Nucleic Acids Res.) + proteins annotated by
UniProtKB/Swiss-Prot as associated with cancer + Cancer Gene Census
(http://www.sanger.ac.uk/genetics/CGP/Census/) were also targeted for
manual curation.
2. Search PubMed/Google Scholar using the gene name (including
synonyms) with accompanying text ‘cancer’ and ‘expression’.
3. Curator reviews title to shortlist articles which appear to contain gene
expression information related to cancer and have full text available.
4. Abstracts are read to identify potential true positive articles. All such
articles are downloaded and read to extract key information such as
cancer type and expression information.
5. All cancer types are then mapped to Disease Ontology terms and added
to the BioXpress database.
III. Manual curation protocol for literature mining
Manual curation protocol of BioXpress
Current statistics:
• 536 papers have been filtered to maintain only those
focusing on human cancer after reading the ‘Abstract’ and
‘Introduction’. Among this subset, only papers including
direct evidence reflecting gene expression differentiation
between normal and cancer tissues were kept.
• Filtering then continued with further inspection of the
‘Materials and Method’ and ‘Results’ sections of each paper.
• Curators cross-check all manual curation processes. In total,
135 papers concerning 87 genes have been added to the
BioXpress database through biocuration
III. Manual curation protocol for literature mining
A closer view of the tables in BioXpress
Automatically-curated entries from NGS
sequencing and manually-curated entries
from literature are shown in one table.
III. Manual curation protocol for literature mining
Highlighting differentially expressed genes across all
cancer types
IV. Pan-cancer analysis based on BioXpress
Highlighting differentially expressed genes based on
number of patients
IV. Pan-cancer analysis based on BioXpress
Pan-cancer clustering of top 50 genes based on Differentially
Expressed (A), Tumor (B), Baseline(C)
IV. Pan-cancer analysis based on BioXpress
Ongoing work and future plans
• Linking to cancer-related mutation database
(BioMuta 2.0, Database 2014).
• Integrate with drug information (DrugVar,
poster No.48)
• As proteomic data become available for
different cancer types through programs
similar to the Clinical Proteomic Tumor
Analysis Consortium (CPTAC) , we will map
such data to the genes
V. Ongoing works and future plans
Acknowledgements
• Funding: NCI and McCormick Genomic and Proteomic Center
• High performance Integrated Virtual Environment (HIVE) team
(GW + FDA/CBER; hive.biochemistry.gwu.edu)
End