Support Vector Training of Protein Alignment Models

Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features such as secondary structure, solvent accessibility, or evolutionary information improve sequence to structure alignment accuracy, but conventional generative estimation techniques for alignment models impose independence assumptions that make these features difficult to include in a principled way. In this paper, we overcome this problem using a Support Vector Machine (SVM) method that provides a well-founded way of estimating complex alignment models with hundred of thousands of parameters. Furthermore, we show that the method can be trained using a variety of loss functions. In a rigorous empirical evaluation, the SVM algorithm outperforms the generative alignment method SSALN, a highly accurate generative alignment model that incorporates structural information. The alignment model learned by the SVM aligns 50% of the residues correctly and aligns over 70% of the residues within a shift of four positions

Correction to: The transcriptome landscape of early maize meiosis

Correction Following publication of the original article [1], the authors reported that the number of genes overlaying the bar graph in Fig. 3A were incorrectly counted and inserted (i.e. including a title tile, and in inverse order). The corrected numbers are below and match with the listed genes supplied in Additional File: Table S2

Gene Evolutionary Trajectories and GC Patterns Driven by Recombination in Zea mays

Recombination occurring during meiosis is critical for creating genetic variation and plays an essential role in plant evolution. In addition to creating novel gene combinations, recombination can affect genome structure through altering GC patterns. In maize (Zea mays) and other grasses, another intriguing GC pattern exists. Maize genes show a bimodal GC content distribution that has been attributed to nucleotide bias in the third, or wobble, position of the codon. Recombination may be an underlying driving force given that recombination sites are often associated with high GC content. Here we explore the relationship between recombination and genomic GC patterns by comparing GC gene content at each of the three codon positions (GC1, GC2, and GC3, collectively termed GCx) to instances of a variable GC-rich motif that underlies double strand break (DSB) hotspots and to meiocyte-specific gene expression. Surprisingly, GCx bimodality in maize cannot be fully explained by the codon wobble hypothesis. High GCx genes show a strong overlap with the DSB hotspot motif, possibly providing a mechanism for the high evolutionary rates seen in these genes. On the other hand, genes that are turned on in meiosis (early prophase I) are biased against both high GCx genes and genes with the DSB hotspot motif, possibly allowing important meiotic genes to avoid DSBs. Our data suggests a strong link between the GC-rich motif underlying DSB hotspots and high GCx genes