AliFilter: a machine learning approach to alignment filtering
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2026
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| _version_ | 1866901813127544832 |
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| author | Bianchini, Giorgio Zhu, Rui Cicconardi, Francesco Moody, Edmund R. R. |
| author_facet | Bianchini, Giorgio Zhu, Rui Cicconardi, Francesco Moody, Edmund R. R. |
| contents | <p>Sequence alignment filtering (or "trimming") consists in removing parts of a DNA or protein alignment to improve the performance of a downstream analysis (such as a phylogenetic reconstruction). Alignment columns are removed because they are deemed to be unsuitable for the analysis, e.g. because they are likely to be the result of mistakes introduced by the sequence alignment software, because they contain no information, or because they contain a high amount of noise.</p> <p>Alignment filtering can be performed by manually inspecting alignments and identifying problematic alignment columns, or by using a variety of software tools (e.g., BMGE <a href="https://doi.org/10.1186/1471-2148-10-210" rel="nofollow">[1]</a>, ClipKIT <a href="https://doi.org/10.1371/journal.pbio.3001007" rel="nofollow">[2]</a>, Gblocks <a href="https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334" rel="nofollow">[3]</a>, Noisy <a href="https://doi.org/10.1186/1748-7188-3-7" rel="nofollow">[4]</a>, trimAL <a href="https://doi.org/10.1093/bioinformatics/btp348" rel="nofollow">[5]</a>; see <a href="https://doi.org/10.1093/sysbio/syv033" rel="nofollow">Tan et al. 2015 [6]</a> for a review of some of these). Compared to manual filtering, automated filtering tools have the advantage of being easily applicable to large datasets and producing consistent results; on the other hand, apart from some customisation settings, they are often a "black box" offering little control over which parts of the alignments are preserved or deleted. Manual filtering, on the other hand, is more time consuming and less reproducible, but allows for a more fine-tuned filtering approach.</p> <p><strong>AliFilter</strong> is a tool to automate a manual filtering approach. Using a machine learning algorithm, AliFilter can create a model from a small set of manually filtered alignments; the model can then be used to reproducibly filter many aligments, simulating the manual filtering approach. The program also comes with a pre-trained model that can be used to filter alignments out of the box.</p> <p>AliFilter is a command-line tool available for Windows, macOS and Linux; it is distributed under a GPLv3 license. An API is also available, which allows programs written in C#, C/C++, Python, R, and JavaScript to use AliFilter models for alignment filtering.</p> <p> </p> <p> </p> <div> </div> <p>[1] Criscuolo, A., Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol Biol 10, 210 (2010). <a href="https://doi.org/10.1186/1471-2148-10-210" rel="nofollow">https://doi.org/10.1186/1471-2148-10-210</a></p> <p>[2] Steenwyk JL, Buida TJ III, Li Y, Shen X-X, Rokas A (2020) ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biol 18(12): e3001007. <a href="https://doi.org/10.1371/journal.pbio.3001007" rel="nofollow">https://doi.org/10.1371/journal.pbio.3001007</a></p> <p>[3] Castresana, J. (2000). Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Molecular Biology and Evolution, 17(4), 540–552. <a href="https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334" rel="nofollow">https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334</a></p> <p>[4] Dress, A.W., Flamm, C., Fritzsch, G. et al. Noisy: Identification of problematic columns in multiple sequence alignments. Algorithms Mol Biol 3, 7 (2008). <a href="https://doi.org/10.1186/1748-7188-3-7" rel="nofollow">https://doi.org/10.1186/1748-7188-3-7</a></p> <p>[5] Salvador Capella-Gutiérrez, José M. Silla-Martínez, Toni Gabaldón, trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses, Bioinformatics, Volume 25, Issue 15, August 2009, Pages 1972–1973, <a href="https://doi.org/10.1093/bioinformatics/btp348" rel="nofollow">https://doi.org/10.1093/bioinformatics/btp348</a></p> <p>[6] Ge Tan, Matthieu Muffato, Christian Ledergerber, Javier Herrero, Nick Goldman, Manuel Gil, Christophe Dessimoz, Current Methods for Automated Filtering of Multiple Sequence Alignments Frequently Worsen Single-Gene Phylogenetic Inference, Systematic Biology, Volume 64, Issue 5, September 2015, Pages 778–791, <a href="https://doi.org/10.1093/sysbio/syv033" rel="nofollow">https://doi.org/10.1093/sysbio/syv033</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18637573 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AliFilter: a machine learning approach to alignment filtering Bianchini, Giorgio Zhu, Rui Cicconardi, Francesco Moody, Edmund R. R. <p>Sequence alignment filtering (or "trimming") consists in removing parts of a DNA or protein alignment to improve the performance of a downstream analysis (such as a phylogenetic reconstruction). Alignment columns are removed because they are deemed to be unsuitable for the analysis, e.g. because they are likely to be the result of mistakes introduced by the sequence alignment software, because they contain no information, or because they contain a high amount of noise.</p> <p>Alignment filtering can be performed by manually inspecting alignments and identifying problematic alignment columns, or by using a variety of software tools (e.g., BMGE <a href="https://doi.org/10.1186/1471-2148-10-210" rel="nofollow">[1]</a>, ClipKIT <a href="https://doi.org/10.1371/journal.pbio.3001007" rel="nofollow">[2]</a>, Gblocks <a href="https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334" rel="nofollow">[3]</a>, Noisy <a href="https://doi.org/10.1186/1748-7188-3-7" rel="nofollow">[4]</a>, trimAL <a href="https://doi.org/10.1093/bioinformatics/btp348" rel="nofollow">[5]</a>; see <a href="https://doi.org/10.1093/sysbio/syv033" rel="nofollow">Tan et al. 2015 [6]</a> for a review of some of these). Compared to manual filtering, automated filtering tools have the advantage of being easily applicable to large datasets and producing consistent results; on the other hand, apart from some customisation settings, they are often a "black box" offering little control over which parts of the alignments are preserved or deleted. Manual filtering, on the other hand, is more time consuming and less reproducible, but allows for a more fine-tuned filtering approach.</p> <p><strong>AliFilter</strong> is a tool to automate a manual filtering approach. Using a machine learning algorithm, AliFilter can create a model from a small set of manually filtered alignments; the model can then be used to reproducibly filter many aligments, simulating the manual filtering approach. The program also comes with a pre-trained model that can be used to filter alignments out of the box.</p> <p>AliFilter is a command-line tool available for Windows, macOS and Linux; it is distributed under a GPLv3 license. An API is also available, which allows programs written in C#, C/C++, Python, R, and JavaScript to use AliFilter models for alignment filtering.</p> <p> </p> <p> </p> <div> </div> <p>[1] Criscuolo, A., Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol Biol 10, 210 (2010). <a href="https://doi.org/10.1186/1471-2148-10-210" rel="nofollow">https://doi.org/10.1186/1471-2148-10-210</a></p> <p>[2] Steenwyk JL, Buida TJ III, Li Y, Shen X-X, Rokas A (2020) ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biol 18(12): e3001007. <a href="https://doi.org/10.1371/journal.pbio.3001007" rel="nofollow">https://doi.org/10.1371/journal.pbio.3001007</a></p> <p>[3] Castresana, J. (2000). Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Molecular Biology and Evolution, 17(4), 540–552. <a href="https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334" rel="nofollow">https://doi.org/10.1093/OXFORDJOURNALS.MOLBEV.A026334</a></p> <p>[4] Dress, A.W., Flamm, C., Fritzsch, G. et al. Noisy: Identification of problematic columns in multiple sequence alignments. Algorithms Mol Biol 3, 7 (2008). <a href="https://doi.org/10.1186/1748-7188-3-7" rel="nofollow">https://doi.org/10.1186/1748-7188-3-7</a></p> <p>[5] Salvador Capella-Gutiérrez, José M. Silla-Martínez, Toni Gabaldón, trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses, Bioinformatics, Volume 25, Issue 15, August 2009, Pages 1972–1973, <a href="https://doi.org/10.1093/bioinformatics/btp348" rel="nofollow">https://doi.org/10.1093/bioinformatics/btp348</a></p> <p>[6] Ge Tan, Matthieu Muffato, Christian Ledergerber, Javier Herrero, Nick Goldman, Manuel Gil, Christophe Dessimoz, Current Methods for Automated Filtering of Multiple Sequence Alignments Frequently Worsen Single-Gene Phylogenetic Inference, Systematic Biology, Volume 64, Issue 5, September 2015, Pages 778–791, <a href="https://doi.org/10.1093/sysbio/syv033" rel="nofollow">https://doi.org/10.1093/sysbio/syv033</a></p> |
| title | AliFilter: a machine learning approach to alignment filtering |
| url | https://doi.org/10.5281/zenodo.18637573 |