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Correction to: SLR: a scaffolding algorithm based on long reads and contig classification

Correction to: BMC Bioinformatics (2019) 20:539

https://doi.org/10.1186/s12859-019-3114-9

Following publication of the original article [1], the author reported that there is an error in the original article;

  1. 1.

    The figures’ order in HTML and PDF are incorrect.

    In the original article incorrect Fig. 1 is the correct Fig. 4.

    In the original article incorrect Fig. 2 is the correct Fig. 5.

    In the original article incorrect Fig. 3 is the correct Fig. 6.

    In the original article incorrect Fig. 4 is the correct Fig. 1.

    In the original article incorrect Fig. 5 is the correct Fig. 2.

    In the original article incorrect Fig. 6 is the correct Fig. 3.

Fig. 1
figure1

Nine figures plotting NGA50 vs Misassemblies. The results of SLR usually can be found in the top-left corner, which can illustrate the advantage of SLR

Fig. 2
figure2

Contig classification combines with SSPACE-LR and LINKS

Fig. 3
figure3

NGA50 for datasets produced by repeat-aware evaluation framework

Fig. 4
figure4

An example of alignment position revision. For an alignment given by the alignment tool, the region [sr11, er11] (region3) in the long read lr1 is aligned with the region [sc11, ec11] (region1) in the contig c1. Because sr11 < sc11 and LEN(lr1) − er11 > LEN(c1) − ec11, it means the region [0, sr11] (region4) in lr1 is not aligned with c1, and the region [ec11,LEN(c1) − 1] (region2) is not aligned with lr1. However, when lr1 is truely aligned with c1 and the alignment is reliable, region4 should be aligned with the region [sc11 − sr11, sc11] in c1, and region2 should be aligned with the region [er11, er11 + LEN(c1) − ec11]. Because of the high sequencing error rate in long reads, the alignment tool usually does not provide accurate alignment start and end positions. Then, SLR sets sc11′ = sc11 − sr11, sr11′ = 0, ec11′ = LEN(c1) − 1 and er11′ = er11 + LEN(c1) − ec11. When the alignment is reliable, the region [sc11′, ec11′] in c1 is aligned with the region [sr11′, er11′] in lr1

Fig. 5
figure5

There are six contigs (c1,c2,c3,c4,c5,andc6) that can be aligned with the long read lr1. Because c1 and c2 are simultaneously aligned with the left end of lr1, SLR retains only contig c1 which has the greatest alignment length, and deletes the alignment information between c2 and lr1. Because c5 and c6 have been simultaneously aligned with the right end of lr1, we keep only c5, and delete the alignment information between c6 and lr1. Finally, SLR determines the orders and orientations of c1, c3, c4 and c5, which form a local scaffold

Fig. 6
figure6

(a) There are six long reads: lr1, lr2, lr3, lr4, lr5, and lr6. The contigs c1 and c2 are aligned with lr1. c3, c4 and c5 are aligned with lr2. c6, c4 and c7 are aligned with lr3. c7, c8 and c9 are aligned with lr4. c10, c11 and c12 are aligned with lr5. c9, c11, c13 and c2 are aligned with lr6. We assume that all these alignments are forward, and all contigs are longer than Lca. (b) Based on the alignment result described in (a), SLR obtains six local scaffolds: ls1, ls2, ls3, ls4, ls5, and ls6. (c) The scaffold graph G1 is built using all contigs. We find that G1 is complicated. (d) Based on the contig classification method described in Section 2.2, the contigs can be divided into two categories. Because c4 is located in the middle position of ls2 and ls3 and has two distinct 3′-end neighbours and two distinct 5′-end neighbour contigs, it is identified as an ambiguous contig. c11 is also an ambiguous contig. The remaining contigs are identified as unique contigs. The scaffold graph G2 is built based on unique contigs and is thus less complicated than G1

In this correction article the figures are shown correct.

Reference

  1. 1.

    Luo J, et al. SLR: a scaffolding algorithm based on long reads and contig classification. BMC Bioinformatics. 2019;20:539. https://doi.org/10.1186/s12859-019-3114-9.

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Correspondence to Junwei Luo.

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Luo, J., Lyu, M., Chen, R. et al. Correction to: SLR: a scaffolding algorithm based on long reads and contig classification. BMC Bioinformatics 21, 50 (2020). https://doi.org/10.1186/s12859-020-3362-8

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