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SINA - reference based multiple sequence alignment

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SINA aligns nucleotide sequences to match a pre-existing MSA using a graph based alignment algorithm similar to PoA. The graph approach allows SINA to incorporate information from many reference sequences building without blurring highly variable regions. While pure NAST implementations depend highly on finding a good match in the reference database, SINA is able to align sequences relatively distant to references with good quality and will yield a robust result for query sequences with many close reference.


  • Speed. Aligning 100,000 full length rRNA against the SILVA NR takes 40 minutes on a mid-sized 2018 desktop computer. Aligning 1,000,000 V4 amplicons takes about 60 minutes.
  • Accuracy. SINA is used to build the SILVA SSU and LSU rRNA databases.
  • Classification. SINA includes an LCA based classification module.
  • ARB. SINA is able to directly read and write ARB format files such as distributed by the SILVA project.

Online Version

An online version for submitting small batches of sequences is made available by the SILVA project as part of their ACT: Alignment, Classification and Tree Service. In addition to SINA's alignment and classification stages, ACT allows directly building phylogenetic trees with RAxML or FastTree from your sequences and (optionally) additional sequences chosen using SINA's add-neighbors feature.

Installing SINA

The preferred way to install SINA locally is via Bioconda. If you have a working Bioconda installation, just run:

conda create -n sina sina
conda activate sina

Alternatively, self-contained images are available at Choose the most recent tar.gz appropriate for your operating system and unpack:

tar xf sina-1.7.3-dev-dev-linux.tar.gz
cd sina-1.7.3-dev-dev


The full documentation is available at

The algorithm is explained in the paper:

Elmar Pruesse, Jörg Peplies, Frank Oliver Glöckner; SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012; 28 (14): 1823-1829. doi:10.1093/bioinformatics/bts252