Evidence map›Paper›PMID 21350909›Full record

ArticleJournal of cardiovascular translational research2011

Meet me halfway: when genomics meets structural bioinformatics.

Sungsam Gong, Catherine L Worth, Tammy M K Cheng, Tom L Blundell

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

4 authors.

Sungsam GongBiocomputing Group, Department of Biochemistry, University of Cambridge, UK. s.gong@rbht.nhs.uk
Catherine L Worth
Tammy M K Cheng
Tom L Blundell

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The DNA sequencing technology developed by Frederick Sanger in the 1970s established genomics as the basis of comparative genetics. The recent invention of next-generation sequencing (NGS) platform has added a new dimension to genome research by generating ultra-fast and high-throughput sequencing data in an unprecedented manner. The advent of NGS technology also provides the opportunity to study genetic diseases where sequence variants or mutations are sought to establish a causal relationship with disease phenotypes. However, it is not a trivial task to seek genetic variants responsible for genetic diseases and even harder for complex diseases such as diabetes and cancers. In such polygenic diseases, multiple genes and alleles, which can exist in healthy individuals, come together to contribute to common disease phenotypes in a complex manner. Hence, it is desirable to have an approach that integrates omics data with both knowledge of protein structure and function and an understanding of networks/pathways, i.e. functional genomics and systems biology; in this way, genotype-phenotype relationships can be better understood. In this review, we bring this 'bottom-up' approach alongside the current NGS-driven genetic study of genetic variations and disease aetiology. We describe experimental and computational techniques for assessing genetic variants and their deleterious effects on protein structure and function.

Indexed as

GenomicsSystems BiologyAnimalsComputer SimulationDatabases, GeneticDNA Mutational AnalysisGene Regulatory NetworksGenetic Predisposition to DiseaseGenetic VariationHigh-Throughput Nucleotide SequencingHumansModels, MolecularMutationPhenotypeProtein ConformationProteinsProteins

Identifiers

What OpenQuestion holds

Textmetadata
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.