Evidence map›Paper›PMID 26516187›Full record

ArticleNucleic acids research2016

SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.

Jing Guo, Hui Liu, Jie Zheng

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 1 synthesis or guideline pooled it, 161 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Knowledge-based citation reasoning for biomedical domain.Bioinformatics (Oxford, England) · 2026
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  5. Frontiers in cell and developmental biology · 2026
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  15. Machine Learning Approaches for the Identification of Genetic Interactions.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  16. Review
  17. Article
  18. Article
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  20. Article

17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Jing GuoSchool of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Hui LiuSchool of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore Lab of Information Management, Changzhou University, Jiangsu 213164, China hliu@cczu.edu.cn.
Jie ZhengSchool of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore Genome Institute of Singapore (GIS), Biopolis, Singapore 138672, Singapore ZhengJie@ntu.edu.sg.
Nanyang Technological University · SGChangzhou University · CN

Funding

Algorithm and genome-wide database of functional siRNAsR44HG003355 · NHGRI · CELLECTA, INC. · PI CHENCHIK, ALEX · 2007 to 2009
$2.5M
Functionally Validated Lentiviral siRNA librariesR44RR024095 · NCRR · CELLECTA, INC. · PI CHENCHIK, ALEX · 2010 to 2011
$1.9M
Functionally Validated Lentiviral siRNA LibrariesR43RR024095 · NCRR · CELLECTA, INC. · PI CHENCHIK, ALEX · 2007 to 2007
$289k
Algorithm and genome-wide database of functional siRNAsR43HG003355 · NHGRI · SYSTEM BIOSCIENCES, LLC (SBI) · PI CHENCHIK, ALEX · 2004 to 2004
$100k
NCRR NIH HHS 44RR024095NCRR NIH HHS R44 RR024095NHGRI NIH HHS 44HG003355NHGRI NIH HHS R44 HG003355
6 · The paper itself

Abstract

Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.

Indexed as

Databases, PharmaceuticalDrug DiscoveryGenes, NeoplasmAnimalsAntineoplastic AgentsCell DeathCell Line, TumorData Interpretation, StatisticalHumansMiceMutationRNA InterferenceAntineoplastic Agents

Identifiers

PMID26516187
PMCPMC4702809
OpenAlexW1895317372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.