Evidence map›Paper›PMID 23066108›Full record

ArticleNucleic acids research2012

LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets.

Andreas Wilm, Pauline Poh Kim Aw, Denis Bertrand, Grace Hui Ting Yeo, Swee Hoe Ong, Chang Hua Wong, Chiea Chuen Khor, Rosemary Petric, Martin Lloyd Hibberd, Niranjan Nagarajan

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 903 papers.

0numbers the graph read from it
0cells of the map it votes in
903citing 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

903 citing papers in PubMed.

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  13. Dynamic evolution and hierarchical regulation of rDNA in polyploid oat.The Plant journal : for cell and molecular biology · 2026
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843 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

10 authors.

Andreas WilmGenome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore 138672, Singapore.
Pauline Poh Kim Aw
Denis Bertrand
Grace Hui Ting Yeo
Swee Hoe Ong
Chang Hua Wong
Chiea Chuen Khor
Rosemary Petric
Martin Lloyd Hibberd
Niranjan Nagarajan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of cell-population heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.

Indexed as

Genetic VariationComputer SimulationDengue VirusEscherichia coliGenomicsHigh-Throughput Nucleotide SequencingHumansMutationSensitivity and SpecificityStomach NeoplasmsViral ProteinsViral Proteins

Identifiers

PMID23066108
PMCPMC3526318

What OpenQuestion holds

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LicenceCC BY-NC
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.