| 1 | SINA - reference based multiple sequence alignment |
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| 2 | ================================================== |
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| 3 | |
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| 4 | |latest| |Bioconda| |downloads| |TravisCI| |CircleCI| |Read the Docs| |Codecov| |
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| 5 | |
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| 6 | .. |latest| image:: https://img.shields.io/github/release/epruesse/SINA/all.svg?label=latest |
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| 7 | .. |release| image:: https://img.shields.io/github/release/epruesse/SINA.svg |
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| 8 | .. |Bioconda| image:: https://img.shields.io/conda/vn/Bioconda/sina.svg |
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| 9 | :target: https://bioconda.github.io/recipes/sina/README.html |
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| 10 | .. |TravisCI| image:: https://img.shields.io/travis/epruesse/SINA.svg?label=build%20(TravisCI) |
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| 11 | :target: https://travis-ci.org/epruesse/SINA |
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| 12 | .. |CircleCI| image:: https://img.shields.io/circleci/project/github/epruesse/SINA.svg?label=build%20(CircleCI) |
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| 13 | :target: https://circleci.com/gh/epruesse/SINA |
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| 14 | .. |Codecov| image:: https://img.shields.io/codecov/c/github/epruesse/sina.svg |
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| 15 | :target: https://codecov.io/gh/epruesse/SINA |
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| 16 | .. |Read the Docs| image:: https://img.shields.io/readthedocs/sina/latest.svg |
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| 17 | :target: https://readthedocs.org/projects/sina/builds |
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| 18 | .. |downloads| image:: https://img.shields.io/conda/dn/bioconda/sina.svg?style=flat |
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| 19 | |
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| 20 | |
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| 21 | SINA aligns nucleotide sequences to match a pre-existing MSA using |
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| 22 | a graph based alignment algorithm similar to PoA. The graph approach |
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| 23 | allows SINA to incorporate information from many reference sequences |
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| 24 | building without blurring highly variable regions. While |
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| 25 | pure NAST implementations depend highly on finding a good match in |
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| 26 | the reference database, SINA is able to align sequences relatively |
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| 27 | distant to references with good quality and will yield a robust result |
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| 28 | for query sequences with many close reference. |
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| 29 | |
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| 30 | Features |
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| 31 | -------- |
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| 32 | |
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| 33 | - 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. |
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| 34 | - Accuracy. SINA is used to build the SILVA_ SSU and LSU rRNA databases. |
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| 35 | - Classification. SINA includes an LCA based classification module. |
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| 36 | - ARB. SINA is able to directly read and write ARB_ format files such as distributed by the SILVA_ project. |
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| 37 | |
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| 38 | .. _SILVA: https://www.arb-silva.de |
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| 39 | .. _ARB: https://www.arb-home.de |
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| 40 | |
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| 41 | Online Version |
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| 42 | -------------- |
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| 43 | |
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| 44 | An online version for submitting small batches of sequences is made |
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| 45 | available by the SILVA_ project as part of their |
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| 46 | `ACT: Alignment, Classification and Tree Service <https://www.arb-silva.de/aligner>`_. |
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| 47 | In addition to SINA's alignment and classification stages, ACT allows directly building |
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| 48 | phylogenetic trees with RAxML or FastTree from your sequences and (optionally) |
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| 49 | additional sequences chosen using SINA's add-neighbors feature. |
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| 50 | |
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| 51 | Installing SINA |
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| 52 | --------------- |
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| 53 | |
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| 54 | The preferred way to install SINA locally is via `Bioconda <https://bioconda.github.io>`_. |
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| 55 | If you have a working Bioconda installation, just run:: |
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| 56 | |
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| 57 | conda create -n sina sina |
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| 58 | conda activate sina |
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| 59 | |
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| 60 | Alternatively, self-contained images are available at |
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| 61 | https://github.com/epruesse/SINA/releases. Choose the most recent ``tar.gz`` |
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| 62 | appropriate for your operating system and unpack:: |
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| 63 | |
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| 64 | tar xf sina-1.7.2-dev-linux.tar.gz |
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| 65 | cd sina-1.7.2-dev |
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| 66 | ./sina |
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| 67 | |
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| 68 | |
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| 69 | Documentation |
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| 70 | ------------- |
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| 71 | |
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| 72 | The full documentation is available at https://sina.readthedocs.io. |
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| 73 | |
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| 74 | The algorithm is explained in the paper: |
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| 75 | |
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| 76 | Elmar Pruesse, Jörg Peplies, Frank Oliver Glöckner; *SINA: Accurate |
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| 77 | high-throughput multiple sequence alignment of ribosomal RNA |
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| 78 | genes.* Bioinformatics 2012; 28 (14): 1823-1829. |
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| 79 | `doi:10.1093/bioinformatics/bts252 <https://doi.org/10.1093/bioinformatics/bts252>`_ |
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