Evidence map›Paper›PMID 16961919›Full record

ArticleBMC bioinformatics2006

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements.

Kannan Tharakaraman, Leonardo Mariño-Ramírez, Sergey L Sheetlin, David Landsman, John L Spouge

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Promoter analysis: gene regulatory motif identification with A-GLAM.Methods in molecular biology (Clifton, N.J.) · 2009
    Article
  2. Review
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 1 country.

Kannan TharakaramanComputational Biology Branch, National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, MSC 6075 Bethesda, MD 20894-6075, USA. tharakar@ncbi.nlm.nih.gov
Leonardo Mariño-Ramírez
Sergey L Sheetlin
David Landsman
John L Spouge
National Institutes of Health · USNational Center for Biotechnology Information · US

Funding

Gene Regulatory Sequences from Whole Chromosomes/genomesZ01LM000084 · NLM · NATIONAL LIBRARY OF MEDICINE · PI LANDSMAN, DAVID · 1998 to 2008
$627k
The Analysis of Signal Elements in Promoter Sequences.Z01LM091704 · NLM · NATIONAL LIBRARY OF MEDICINE · PI SPOUGE, JOHN L · 2004 to 2008
$523k
Intramural NIH HHS Z99 LM999999
6 · The paper itself

Abstract

backgroundMany DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set.

resultsWe describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional "scanning step" after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix (PSSM). The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an "individual score" to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence.

conclusionDatasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances.

Indexed as

Markov ChainsMonte Carlo MethodAnimalsBase SequenceDrosophilaMolecular Sequence DataRegulatory Elements, TranscriptionalSaccharomycesSequence AlignmentSequence Analysis, DNA

Identifiers

PMID16961919
PMCPMC1599759
OpenAlexW1496558458

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.