Evidence map›Paper›PMID 23919388›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2013

Fast matching of transcription factor motifs using generalized position weight matrix models.

Emanuele Giaquinta, Szymon Grabowski, Esko Ukkonen

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Emanuele GiaquintaDepartment of Computer Science, University of Helsinki, Helsinki, Finland. emanuele.giaquinta@cs.helsinki.fi
Szymon Grabowski
Esko Ukkonen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The problem of finding the locations in DNA sequences that match a given motif describing the binding specificities of a transcription factor (TF) has many applications in computational biology. This problem has been extensively studied when the position weight matrix (PWM) model is used to represent motifs. We investigate it under the feature motif model, a generalization of the PWM model that does not assume independence between positions in the pattern while being compatible with the original PWM. We present a new method for finding the binding sites of a transcription factor in a DNA sequence when the feature motif model is used to describe transcription factor binding specificities. The experimental results on random and real data show that the search algorithm is fast in practice.

Indexed as

Models, GeneticAmino Acid MotifsComputational BiologyResponse ElementsTranscription FactorsTranscription Factors

Identifiers

PMID23919388
PMCPMC3761436

What OpenQuestion holds

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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.