Evidence map›Paper›PMID 23281792›Full record

ArticleBMC bioinformatics2012

TIGER: tiled iterative genome assembler.

Xiao-Long Wu, Yun Heo, Izzat El Hajj, Wen-Mei Hwu, Deming Chen, Jian Ma

Abstract read
In one paragraph

Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Xiao-Long WuDepartment of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Yun Heo
Izzat El Hajj
Wen-Mei Hwu
Deming Chen
Jian Ma

Funding

IMPROVE GENOME ANNOTATION USING MULTIPLE SEQUENCE ALIGNMENT RELIABILITY SCORESR21HG006464 · NHGRI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MA, JIAN · 2012 to 2013
$383k
NHGRI NIH HHS 1R21HG006464
6 · The paper itself

Abstract

backgroundWith the cost reduction of the next-generation sequencing (NGS) technologies, genomics has provided us with an unprecedented opportunity to understand fundamental questions in biology and elucidate human diseases. De novo genome assembly is one of the most important steps to reconstruct the sequenced genome. However, most de novo assemblers require enormous amount of computational resource, which is not accessible for most research groups and medical personnel.

resultsWe have developed a novel de novo assembly framework, called Tiger, which adapts to available computing resources by iteratively decomposing the assembly problem into sub-problems. Our method is also flexible to embed different assemblers for various types of target genomes. Using the sequence data from a human chromosome, our results show that Tiger can achieve much better NG50s, better genome coverage, and slightly higher errors, as compared to Velvet and SOAPdenovo, using modest amount of memory that are available in commodity computers today.

conclusionsMost state-of-the-art assemblers that can achieve relatively high assembly quality need excessive amount of computing resource (in particular, memory) that is not available to most researchers to achieve high quality results. Tiger provides the only known viable path to utilize NGS de novo assemblers that require more memory than that is present in available computers. Evaluation results demonstrate the feasibility of getting better quality results with low memory footprint and the scalability of using distributed commodity computers.

Indexed as

AnimalsContig MappingGenomeGenomicsHumansSequence Analysis, DNA

Identifiers

PMID23281792
PMCPMC3526431

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.