Evidence map›Paper›PMID 23870653›Full record

ArticleGigaScience2013

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species.

Keith R Bradnam, Joseph N Fass, Anton Alexandrov, Paul Baranay, Michael Bechner, Inanç Birol, Sébastien Boisvert, Jarrod A Chapman, Guillaume Chapuis, Rayan Chikhi and 81 more

Abstract read
In one paragraph

Article in GigaScience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 344 papers.

0numbers the graph read from it
0cells of the map it votes in
344citing 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

344 citing papers in PubMed.

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  18. Selection on the vascular-remodelingEvolution letters · 2025
    Article
  19. Article
  20. Article

284 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

91 authors.

Keith R BradnamGenome Center, UC, Davis, CA 95616, USA. krbradnam@ucdavis.edu.
Joseph N Fass
Anton Alexandrov
Paul Baranay
Michael Bechner
Inanç Birol
Sébastien Boisvert
Jarrod A Chapman
Guillaume Chapuis
Rayan Chikhi
Hamidreza Chitsaz
Wen-Chi Chou
Jacques Corbeil
Cristian Del Fabbro
T Roderick Docking
Richard Durbin
Dent Earl
Scott Emrich
Pavel Fedotov
Nuno A Fonseca
Ganeshkumar Ganapathy
Richard A Gibbs
Sante Gnerre
Elénie Godzaridis
Steve Goldstein
Matthias Haimel
Giles Hall
David Haussler
Joseph B Hiatt
Isaac Y Ho
Jason Howard
Martin Hunt
Shaun D Jackman
David B Jaffe
Erich D Jarvis
Huaiyang Jiang
Sergey Kazakov
Paul J Kersey
Jacob O Kitzman
James R Knight
Sergey Koren
Tak-Wah Lam
Dominique Lavenier
François Laviolette
Yingrui Li
Zhenyu Li
Binghang Liu
Yue Liu
Ruibang Luo
Iain Maccallum
Matthew D Macmanes
Nicolas Maillet
Sergey Melnikov
Delphine Naquin
Zemin Ning
Thomas D Otto
Benedict Paten
Octávio S Paulo
Adam M Phillippy
Francisco Pina-Martins
Michael Place
Dariusz Przybylski
Xiang Qin
Carson Qu
Filipe J Ribeiro
Stephen Richards
Daniel S Rokhsar
J Graham Ruby
Simone Scalabrin
Michael C Schatz
David C Schwartz
Alexey Sergushichev
Ted Sharpe
Timothy I Shaw
Jay Shendure
Yujian Shi
Jared T Simpson
Henry Song
Fedor Tsarev
Francesco Vezzi
Riccardo Vicedomini
Bruno M Vieira
Jun Wang
Kim C Worley
Shuangye Yin
Siu-Ming Yiu
Jianying Yuan
Guojie Zhang
Hao Zhang
Shiguo Zhou
Ian F Korf

Funding

The Human Genome Sequencing CenterU54HG003273 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI GIBBS, RICHARD A · 2004 to 2015
$341.3M
University of Washington Medical Scientist Training Program: MD/PhDT32GM007266 · NIGMS · UNIVERSITY OF WASHINGTON · PI HORWITZ, MARSHALL S. · 1985 to 2023
$30.4M
Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
Advances in computational gene findingR01HG004348 · NHGRI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KORF, IAN F · 2007 to 2011
$1.7M
NHGRI NIH HHS R01 HG004348NHGRI NIH HHS R01 HG006677NHGRI NIH HHS U54 HG003273NIGMS NIH HHS T32 GM007266
6 · The paper itself

Abstract

backgroundThe process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly.

resultsIn Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies.

conclusionsMany current genome assemblers produced useful assemblies, containing a significant representation of their genes and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another.

Identifiers

PMID23870653
PMCPMC3844414

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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.