Purifying selection against spurious splicing signals contributes to the base composition evolution of the polypyrimidine tract

Yıldırım, Burçin; Vogl, Claus

doi:10.1111/jeb.14205

Cited by 3 publications

(3 citation statements)

References 80 publications

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“…Driving k-mers from these topics (Figure 3D) and sequence logos generated for the upstream region (Figure 3G, 3H) indicate that 1) the long intron group has a longer polypyrimidine tract, which is consistent with the observation of previous study (Yıldırım & Vogl, 2023); 2) topic 2, enriched in long introns, is characterized by runs of T in the pyrimidine tract (-20 to -5) while topic 1 is characterized by runs of C in the pyrimidine tract (-6 to -3); 3) topic 1 is characterized by GC-rich sequences upstream (-50 to -44). These differences characterize the most distinctive k-mers; there are many other differences.…”

Section: Resultssupporting

confidence: 88%

Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

Wang,

Mount

2023

Preprint

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Strings of nucleotides that carry biological information are typically described using sequence motifs that can be represented by weight matrices or consensus sequences. However, many biological signals in DNA or RNA are recognized by multiple factors in temporal sequence, consist of a mixture of sometimes dissimilar alternative motifs, or may be described better by base composition. Here we apply the Latent Dirichlet Allocation (LDA) mixture model to nucleotide sequences, using k-mers as features, in three related approaches. First, positions in aligned sequences are used as samples. Alternatively, whole sequences are used as samples, either with positional k-mers or with bulk k-mers occurring throughout the sequence. LDA readily identifies motifs, including such elusive cases as the intron branch site. LDA can also identify subtypes of sequence, such as splice site subtypes enriched in long vs. short introns, and can reliably distinguish such properties as reading frame or species of origin. Our results show that LDA is a useful model for describing heterogeneous signals, for assigning individual sequences to subtypes, and for identifying and characterizing sequences that do not fit recognized subtypes. Because LDA topic models are interpretable, they also aid the discovery of new motifs, even those present in a small fraction of samples, allowing a user to hypothesize potential regulatory factors. In summary, LDA can identify and characterize signals in nucleotide sequences and is useful for identifying candidate regulatory factors involved in complex biological processes.

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Section: Resultssupporting

confidence: 88%

Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

Wang,

Mount

2023

Preprint

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“…Synonymous codons are also subject to selection due to codon usage bias (Akashi, 1994, 1995) with variable intensity depending on species and codons (Singh et al, 2009; Zeng, 2010). So far, the best candidates for neutrally evolving, unconstrained sites in Drosophila are the nucleotides at position 8-30 on the 5’ end of introns shorter than 65 bp (hereafter 5SI) (Halligan & Keightley, 2006; Parsch et al, 2010; Clemente & Vogl, 2012; Yıldırım & Vogl, 2023). These sites exhibit higher divergence and polymorphism levels compared to other regions in introns (Parsch et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…These sites exhibit higher divergence and polymorphism levels compared to other regions in introns (Parsch et al, 2010). Longer introns contain more functional elements, as shown by a negative correlation between divergence and length (Haddrill et al, 2005) and most other sequences inside short introns are likely under selection due to their association with splicing (Yıldırım & Vogl, 2023). Thus, many studieswere based on the premise that 5SI sequences evolve neutrally and therefore can be used to infer directional selection on fourfold degenerate sites (Lawrie et al, 2013; Machado et al, 2020) or demography before detecting sweep signatures (Garud et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The influence ofGC-biased gene conversion on nonadaptive sequence evolution in short introns ofDrosophila melanogaster

Yıldırım

Vogl

2023

Preprint

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Population genetic inference of selection on the nucleotide sequence level often assumes comparison to unconstrained nucleotide sites, evolving only under mutation and population demography. Among the few candidates for such a reference sequence is the 5' part of short introns (5SI) in Drosophila. In addition to mutation and population demography, however, there is evidence for a weak force favoring GC bases, likely due to GC-biased gene conversion (gBGC), and for the effect of linked selection. Here, we use polymorphism and divergence data of Drosophila melanogaster to detect and describe the forces affecting the evolution the 5SI. We separately analyze mutation classes, compare them between chromosomes, and relate them to recombination rate frequencies. GC-conservative mutations seem to be mainly influenced by mutation and drift, while linked selection mostly causes differences between the central and the peripheral (i.e., telomeric and centromeric) regions of the chromosome arms. Comparing GC-conservative mutation patterns between autosomes and the X chromosome, showed differences in mutation rates, rather than linked selection, in the central chromosomal regions after accounting for differences in autosomal and X chromosomal effective populations sizes. On the other hand, GC-changing mutations show asymmetric site frequency spectra, indicating the presence of gBGC, varying among mutation classes and in amount along chromosomes, but approximately equal in strength in autosomes and the X chromosome.

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Latent Dirichlet allocation mixture models for nucleotide sequence analysis

Wang,

Mount

2024

NAR Genomics and Bioinformatics

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Strings of nucleotides carrying biological information are typically described as sequence motifs represented by weight matrices or consensus sequences. However, many signals in DNA or RNA are recognized by multiple factors in temporal sequence, consist of distinct alternative motifs, or are best described by base composition. Here we apply the latent Dirichlet allocation (LDA) mixture model to nucleotide sequences. Using positions in an alignment of human or Drosophila splice sites as samples, we show that LDA readily identifies motifs, including such elusive cases as the intron branch site. Using whole sequences with positional k-mers as features, LDA can identify sequence subtypes enriched in long vs. short introns. LDA with bulk k-mers can reliably distinguish reading frame and species of origin in coding sequences from humans and Drosophila. We find that LDA is a useful model for describing heterogeneous signals, for assigning individual sequences to subtypes, and for identifying and characterizing sequences that do not fit recognized subtypes. Because LDA topic models are interpretable, they also aid the discovery of new motifs, even those present in a small fraction of samples. In summary, LDA can identify and characterize signals in nucleotide sequences, including candidate regulatory factors involved in biological processes.

show abstract

Purifying selection against spurious splicing signals contributes to the base composition evolution of the polypyrimidine tract

Cited by 3 publications

References 80 publications

Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

The influence ofGC-biased gene conversion on nonadaptive sequence evolution in short introns ofDrosophila melanogaster

Latent Dirichlet allocation mixture models for nucleotide sequence analysis

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