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Open Access Highly Accessed Methodology article

Sequencing error correction without a reference genome

Julie A Sleep12*, Andreas W Schreiber34 and Ute Baumann1

Author Affiliations

1 Australian Centre for Plant Functional Genomics, The University of Adelaide, Urrbrae, SA 5064, Australia

2 Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia

3 ACRF South Australian Cancer Genome Facility, Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia

4 School of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA 5000, Australia

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BMC Bioinformatics 2013, 14:367  doi:10.1186/1471-2105-14-367

Published: 18 December 2013

Additional files

Additional file 1:

Connected subgraph of sequences. A connected subgraph of sequences of length 21 from an Illumina HiSeq data set. The most abundant sequence in this subgraph occurred 45,484 times and is represented by the largest node (filled circle).

Format: EPS Size: 157KB Download file

Open Data

Additional file 2:

A larger connected subgraph. A connected subgraph of sequences of length 21 from an Illumina GA data set. The most abundant sequence in this subgraph occurred 165,504 times in the data set. The size of the nodes (filled circles) representing each sequence is proportional to their abundance. The edges connect sequences that vary in one position only.

Format: EPS Size: 464KB Download file

Open Data

Additional file 3:

Modelled error rates. Modelled error rates from a data set with simulated errors according to the pattern found in the data set of Figure 3(a).

Format: EPS Size: 13KB Download file

Open Data