Evidence map›Paper›PMID 21918614›Full record

ArticleAdvances and applications in bioinformatics and chemistry : AABC2009

On calculating the probability of a set of orthologous sequences.

Junfeng Liu, Liang Chen, Hongyu Zhao, Dirk F Moore, Yong Lin, Weichung Joe Shih

Abstract read
In one paragraph

Article in Advances and applications in bioinformatics and chemistry : AABC, 2009. 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

6 authors.

Junfeng LiuBiometrics Division, The Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Liang Chen
Hongyu Zhao
Dirk F Moore
Yong Lin
Weichung Joe Shih

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman-Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment.

Indexed as

evolutionJukes-Cantor modelMonte Carlo integrationNeedleman-Wunsch alignmentorthologous

Identifiers

PMID21918614
PMCPMC3169941

What OpenQuestion holds

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Registered trials

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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.