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Abstract Sequence alignment is an essential method in bioinformatics and the basis of many analyses, including phylogenetic inference, ancestral sequence reconstruction, and gene annotation. Sequencing artifacts and errors made during genome assembly, such as abiological frameshifts and incorrect early stop codons, can impact downstream analyses leading to erroneous conclusions in comparative and functional genomic studies. More significantly, while indels can occur both within and between codons in natural sequences, most amino-acid- and codon-based aligners assume that indels only occur between codons. This mismatch between biology and alignment algorithms produces suboptimal alignments and errors in downstream analyses. To address these issues, we present COATi, a statistical, codon-aware pairwise aligner that supports complex insertion–deletion models and can handle artifacts present in genomic data. COATi allows users to reduce the amount of discarded data while generating more accurate sequence alignments. COATi can infer indels both within and between codons, leading to improved sequence alignments. We applied COATi to a dataset containing orthologous protein-coding sequences from humans and gorillas and conclude that 41% of indels occurred between codons, agreeing with previous work in other species. We also applied COATi to semiempirical benchmark alignments and find that it outperforms several popular alignment programs on several measures of alignment quality and accuracy.
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TAPER: Pinpointing errors in multiple sequence alignments despite varying rates of evolution
Abstract Erroneous data can creep into sequence datasets for reasons ranging from contamination to annotation and alignment mistakes and reduce the accuracy of downstream analyses. As datasets keep getting larger, it has become difficult to check multiple sequence alignments visually for errors, and thus, automatic error detection methods are needed more than ever before. Alignment masking methods, which are widely used, remove entire aligned sites and may reduce signal as much as or more than they reduce the noise.The alternative we propose here is a surprisingly under‐explored approach: looking for errors in small species‐specific stretches of the multiple sequence alignments. We introduce a method called TAPER that uses a novel two‐dimensional outlier detection algorithm. Importantly, TAPER adjusts its null expectations per site and species, and in doing so, it attempts to distinguish the real heterogeneity (signal) from errors (noise).Our results show that TAPER removes very little data yet finds much of the error. The effectiveness of TAPER depends on several properties of the alignment (e.g. evolutionary divergence levels) and the errors (e.g. their length).By enabling data clean up with minimal loss of signal, TAPER can improve downstream analyses such as phylogenetic reconstruction and selection detection. Data errors, small or large, can reduce confidence in the downstream results, and thus, eliminating them can be beneficial even when downstream analyses are not impacted.
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- Award ID(s):
- 1845967
- PAR ID:
- 10448314
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 11
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 2145-2158
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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