Evidence map›Paper›PMID 16757580›Full record

ArticleNucleic acids research2006

An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs.

Yi Xing, Tianwei Yu, Ying Nian Wu, Meenakshi Roy, Joseph Kim, Christopher Lee

Abstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.

0numbers the graph read from it
0cells of the map it votes in
106citing 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

106 citing papers in PubMed.

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46 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.

Yi XingMolecular Biology Institute, Center for Computational Biology, Department of Chemistry and Biochemistry, University of California Los Angeles, USA. yxing@ucla.edu
Tianwei Yu
Ying Nian Wu
Meenakshi Roy
Joseph Kim
Christopher Lee

Funding

Center for Computational Biology (RMI)U54RR021813 · NCRR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TOGA, ARTHUR W · 2004 to 2010
$25.3M
NCRR NIH HHS U54 RR021813NCRR NIH HHS U54-RR021813
6 · The paper itself

Abstract

Reconstructing full-length transcript isoforms from sequence fragments (such as ESTs) is a major interest and challenge for bioinformatic analysis of pre-mRNA alternative splicing. This problem has been formulated as finding traversals across the splice graph, which is a directed acyclic graph (DAG) representation of gene structure and alternative splicing. In this manuscript we introduce a probabilistic formulation of the isoform reconstruction problem, and provide an expectation-maximization (EM) algorithm for its maximum likelihood solution. Using a series of simulated data and expressed sequences from real human genes, we demonstrate that our EM algorithm can correctly handle various situations of fragmentation and coupling in the input data. Our work establishes a general probabilistic framework for splice graph-based reconstructions of full-length isoforms.

Indexed as

AlgorithmsAlternative SplicingChromosomes, Human, Pair 22Computer SimulationExpressed Sequence TagsHLA-D AntigensHumansLikelihood FunctionsProtein IsoformsTropomyosinHLA-D AntigensHLA-DM antigensProtein IsoformsTPM1 protein, humanTropomyosin

Identifiers

PMID16757580
PMCPMC1475746

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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.