Evidence map›Paper›PMID 34880424›Full record

ReviewNature reviews. Genetics2022

Computational analysis of cancer genome sequencing data.

Isidro Cortés-Ciriano, Doga C Gulhan, Jake June-Koo Lee, Giorgio E M Melloni, Peter J Park

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
7.1field-weighted citation impact, top 2% 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

66 citing papers in PubMed, 118 citations in OpenAlex.

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  7. Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026
    Review
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6 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

5 authors at 2 institutions in 2 countries.

Isidro Cortés-CirianoEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
Doga C GulhanDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5419-0549
Jake June-Koo LeeDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1348-4094
Giorgio E M MelloniDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Peter J ParkDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_park@hms.harvard.edu.ORCID http://orcid.org/0000-0001-9378-960X
Harvard University · USEuropean Bioinformatics Institute · GB

Funding

Cancer Research UK
6 · The paper itself

Abstract

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Indexed as

NeoplasmsChromosome MappingComputational BiologyDNA Copy Number VariationsGenome, HumanHigh-Throughput Nucleotide SequencingHumansMutation

Identifiers

PMID34880424
OpenAlexW4200028151

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.