Evidence map›Paper›PMID 16243783›Full record

ArticleNucleic acids research2005

Draft versus finished sequence data for DNA and protein diagnostic signature development.

Shea N Gardner, Marisa W Lam, Jason R Smith, Clinton L Torres, Tom R Slezak

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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  3. Comparative Genomic Analysis ofInternational journal of genomics · 2019
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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 1 institution in 1 country.

Shea N GardnerPathogen Bio-Informatics, Lawrence Livermore National Laboratory, PO Box 808, L-174, Livermore, CA 94551, USA. gardner26@llnl.gov
Marisa W Lam
Jason R Smith
Clinton L Torres
Tom R Slezak
Lawrence Livermore National Laboratory · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10(-3)-10(-5) (approximately 8x coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of approximately 1% (3x to 6x coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures.

Indexed as

Genome, BacterialGenome, ViralComputational BiologyGenomicsMarburgvirusPhylogenySequence AlignmentSequence Analysis, DNASequence Analysis, ProteinSoftwareVariola virusViral ProteinsViral Proteins

Identifiers

PMID16243783
PMCPMC1266063
OpenAlexW2168171328

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.