Evidence map›Paper›PMID 22099972›Full record

ArticleBMC bioinformatics2011

Identification and correction of systematic error in high-throughput sequence data.

Frazer Meacham, Dario Boffelli, Joseph Dhahbi, David I K Martin, Meromit Singer, Lior Pachter

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers.

0numbers the graph read from it
0cells of the map it votes in
137citing 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

137 citing papers in PubMed.

  1. Article
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  8. A complete diploid human genome benchmark for personalized genomics.bioRxiv : the preprint server for biology · 2025
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  11. UnderstandingmedRxiv : the preprint server for health sciences · 2025
    Article
  12. Review
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77 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

6 authors.

Frazer MeachamDepartment of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.
Dario Boffelli
Joseph Dhahbi
David I K Martin
Meromit Singer
Lior Pachter

Funding

Identification of Primate-Specific Regulatory Elements of Cholesterol HomeostasisR01HL084474 · NHLBI · CHILDREN'S HOSPITAL & RES CTR AT OAKLAND · PI BOFFELLI, DARIO · 2007 to 2011
$2.0M
Methods for the analysis of RNA-Seq and related sequence census based experimentsR01HG006129 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI PACHTER, LIOR S · 2011 to 2013
$1.1M
An Assay to Identify and Classify EpimutagensR21ES016581 · NIEHS · CHILDREN'S HOSPITAL & RES CTR AT OAKLAND · PI MARTIN, DAVID IAN KINGSTON · 2008 to 2009
$440k
NHGRI NIH HHS 1R01HG006129-01NHLBI NIH HHS HL084474NIEHS NIH HHS ES016581
6 · The paper itself

Abstract

backgroundA feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations.

resultsWe characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets.

conclusionsSystematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments.

Indexed as

Genetic VariationGenome, HumanHigh-Throughput Nucleotide SequencingHumansNucleotide MotifsPolymorphism, Single NucleotideResearch DesignSequence Analysis, DNASequence Analysis, RNATranscriptome

Identifiers

PMID22099972
PMCPMC3295828

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.