Abstract
Detection of sequences that are homologous, i.e. descended from a common ancestor, is a fundamental task in computational biology. This task is confounded by low-complexity tracts (such as atatatatatat), which arise frequently and independently, causing strong similarities that are not homologies. There has been much research on identifying low-complexity tracts, but little research on how to treat them during homology search. We propose to find homologies by aligning sequences with “gentle” masking of low-complexity tracts. Gentle masking means that the match score involving a masked letter is , where is the unmasked score. Gentle masking slightly but noticeably improves the sensitivity of homology search (compared to “harsh” masking), without harming specificity. We show examples in three useful homology search problems: detection of NUMTs (nuclear copies of mitochondrial DNA), recruitment of metagenomic DNA reads to reference genomes, and pseudogene detection. Gentle masking is currently the best way to treat low-complexity tracts during homology search.
Highlights
The problem of false homology prediction due to lowcomplexity sequences is sufficiently severe that it has been addressed since the early days of computational biology
We recently showed that standard methods such as SegMasker and DustMasker are not perfect: they fail to mask some low-complexity sequences, which produce strong (E-value v10{30), non-homologous alignments [1]
We looked for NUMTs in several nuclear genomes, using either harsh masking or gentle masking of low-complexity regions identified by tantan
Summary
The problem of false homology prediction due to lowcomplexity sequences is sufficiently severe that it has been addressed since the early days of computational biology. Methods to avoid this problem can be classified into three approaches: Hard masking The first approach is to identify low-complexity regions by some means, and replace each letter in these regions with a dummy letter, typically X for proteins and N for DNA. In the NCBI blosum matrices, X receives a score of 21. This prevents low-complexity regions from getting high alignment scores. We described a new masking method, tantan, which prevents non-homologous alignments much more reliably
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