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. 2012 Jul 15;28(14):1823-9.
doi: 10.1093/bioinformatics/bts252. Epub 2012 May 3.

SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes

Elmar Pruesse  1 , Jörg Peplies, Frank Oliver Glöckner
Affiliations

SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes

Elmar Pruesse et al. Bioinformatics. .

Abstract

Motivation: In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements.

Results: In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands. SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks.

Availability: Alignment of up to 500 sequences using the latest SILVA SSU/LSU Ref datasets as reference MSA is offered at http://www.arb-silva.de/aligner. This page also links to Linux binaries, user manual and tutorial. SINA is made available under a personal use license.

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Figures

Fig. 1.
Fig. 1.
The alignment of the selected reference sequences is converted from RC-MSA representation (top) to PO-MSA representation (bottom)
Fig. 2.
Fig. 2.
SINA alignment accuracy decreases almost linearly with the shared fractional identity of candidate and reference when using one reference sequence (red line). Using larger numbers of reference sequences markedly increases accuracy
Fig. 3.
Fig. 3.
An alternative implementation which used simple column-profiles built from the selected reference sequences showed overall lower accuracy. Increasing the number of reference sequences quickly led to a degradation in accuracy
See this image and copyright information in PMC

References

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