Evidence map›Paper›PMID 27376489›Full record

ReviewNature reviews. Genetics2016

Computational genomics tools for dissecting tumour-immune cell interactions.

Hubert Hackl, Pornpimol Charoentong, Francesca Finotello, Zlatko Trajanoski

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
158citing papers in PubMed, 3 pooled it
–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

158 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. The M2 macrophage markerOncoimmunology · 2020
    Pooled it
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98 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

4 authors.

Hubert HacklDivision of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
Pornpimol CharoentongDivision of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
Francesca FinotelloDivision of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
Zlatko TrajanoskiDivision of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour-immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines.

Indexed as

AnimalsCell CommunicationChemokinesComputational BiologyGenomicsHumansNeoplasmsChemokines

Identifiers

PMID27376489

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.