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Improved gap size estimation for scaffolding algorithms
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-7378-2320
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
2012 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 28, no 17, 2215-2222 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: One of the important steps of genome assembly is scaffolding, in which contigs are linked using information from read-pairs. Scaffolding provides estimates about the order, relative orientation and distance between contigs. We have found that contig distance estimates are generally strongly biased and based on false assumptions. Since erroneous distance estimates can mislead in subsequent analysis, it is important to provide unbiased estimation of contig distance.Results: In this article, we show that state-of-the-art programs for scaffolding are using an incorrect model of gap size estimation. We discuss why current maximum likelihood estimators are biased and describe what different cases of bias we are facing. Furthermore, we provide a model for the distribution of reads that span a gap and derive the maximum likelihood equation for the gap length. We motivate why this estimate is sound and show empirically that it outperforms gap estimators in popular scaffolding programs. Our results have consequences both for scaffolding software, structural variation detection and for library insert-size estimation as is commonly performed by read aligners.

Place, publisher, year, edition, pages
2012. Vol. 28, no 17, 2215-2222 p.
National Category
Bioinformatics and Systems Biology
Research subject
SRA - E-Science (SeRC)
URN: urn:nbn:se:kth:diva-101249DOI: 10.1093/bioinformatics/bts441ISI: 000308019200001ScopusID: 2-s2.0-84865546399OAI: diva2:546930
Swedish Research Council, 2010-4634Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish e‐Science Research Center

QC 20120912

Available from: 2012-08-25 Created: 2012-08-25 Last updated: 2015-09-15Bibliographically approved
In thesis
1. Algorithms and statistical models for scaffolding contig assemblies and detecting structural variants using read pair data
Open this publication in new window or tab >>Algorithms and statistical models for scaffolding contig assemblies and detecting structural variants using read pair data
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Advances in throughput from Next Generation Sequencing (NGS) methods has provided new ways to study molecular biology. The increased amount of data enables genome wide scale studies of structural variation, transcription, translation and genome composition. Not only is the scale of each experiment large; lowered cost and faster turn-around has also increased the frequency with which new experiments are conducted. With the data growth comes an increase in demand for efficient and robust algorithms — this is a great computational challenge. The design of computationally efficient algorithms are crucial to cope with the amount of data and it is relatively easy to verify an efficient algorithm by runtime and memory consumption. However, as NGS data comes with several artifacts together with the size the difficulty lies in verifying that the algorithm gives accurate results and are robust to different data sets.

This thesis focuses on modeling assumptions of mate-pair and paired-end reads when scaffolding contig assemblies or detecting variants. Both genome assembly and structural variation are difficult problems, partly because of a computationally complex nature of the problems, but also due to various noise and artifacts in input data. Constructing methods that addresses all artifacts and parameters in data is difficult, if not impossible, and end-to-end pipelines often come with several simplifications. Instead of tackling these difficult problems all at once, a large part of this thesis concentrates on smaller problems around scaffolding and structural variation detection. By identifying and modeling parts of the problem where simplifications has been made in other algorithms, we obtain an improved solution to the corresponding full problem.

The first paper shows an improved model to estimate gap sizes, hence contig placement, in the scaffolding problem. The second paper introduces a new scaffolder to scaffold large complex genomes and the third paper extends the scaffolding method to account for paired-end-contamination in mate-pair libraries. The fourth paper investigates detection of structural variants using fragment length information and corrects a commonly assumed null-hypothesis distribution used to detect structural variants.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. x, 59 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2015:14
National Category
Bioinformatics (Computational Biology)
urn:nbn:se:kth:diva-173580 (URN)978-91-7595-677-0 (ISBN)
Public defence
2015-10-01, Atrium, Nobels väg 12B, Stockholm, 10:00 (English)

QC 20150915

Available from: 2015-09-15 Created: 2015-09-14 Last updated: 2015-09-15Bibliographically approved

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Sahlin, KristofferLundeberg, JoakimArvestad, Lars
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