Evidence map›Paper›PMID 29792514›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2018

A Flow Procedure for Linearization of Genome Sequence Graphs.

David Haussler, Maciej Smuga-Otto, Jordan M Eizenga, Benedict Paten, Adam M Novak, Sergei Nikitin, Maria Zueva, Dmitrii Miagkov

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. A Flow Procedure for Linearization of Genome Sequence Graphs.Journal of computational biology : a journal of computational molecular cell biology · 2018
    Article
  3. Coordinate systems for supergenomes.Algorithms for molecular biology : AMB · 2018
    Article
  4. 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

8 authors.

David Haussler1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
Maciej Smuga-Otto1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
Jordan M Eizenga1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
Benedict Paten1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
Adam M Novak1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
Sergei Nikitin2 EPAM Systems, Inc. , Newtown, Pennsylvania.
Maria Zueva2 EPAM Systems, Inc. , Newtown, Pennsylvania.
Dmitrii Miagkov2 EPAM Systems, Inc. , Newtown, Pennsylvania.

Funding

Training SubprojectU54HG007990 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, PATTERSON, DAVID · 2014 to 2018
$14.8M
NHGRI NIH HHS U54 HG007990
6 · The paper itself

Abstract

Efforts to incorporate human genetic variation into the reference human genome have converged on the idea of a graph representation of genetic variation within a species, a genome sequence graph. A sequence graph represents a set of individual haploid reference genomes as paths in a single graph. When that set of reference genomes is sufficiently diverse, the sequence graph implicitly contains all frequent human genetic variations, including translocations, inversions, deletions, and insertions. In representing a set of genomes as a sequence graph, one encounters certain challenges. One of the most important is the problem of graph linearization, essential both for efficiency of storage and access, and for natural graph visualization and compatibility with other tools. The goal of graph linearization is to order nodes of the graph in such a way that operations such as access, traversal, and visualization are as efficient and effective as possible. A new algorithm for the linearization of sequence graphs, called the flow procedure (FP), is proposed in this article. Comparative experimental evaluation of the FP against other algorithms shows that it outperforms its rivals in the metrics most relevant to sequence graphs.

Indexed as

AlgorithmsBase SequenceChromosome MappingComputational BiologyGenome, HumanGenomicsHumansTranslocation, Geneticbackbonecut widthfeedback arcsflow proceduregroominglinearizationsequence graph.

Identifiers

PMID29792514
PMCPMC6067104

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.