Mumemto: efficient maximal matching across pangenomes.

Aligning genomes into common coordinates is central to pangenome analysis and construction, but it is also computationally expensive. Multi-sequence maximal unique matches (multi-MUMs) are guideposts for core genome alignments, helping to frame and solve the multiple alignment problem. We introduce Mumemto, a tool that computes multi-MUMs and other match types across large pangenomes.

Improved pangenomic classification accuracy with chain statistics.

Compressed full-text indexes enable efficient sequence classification against a pangenome or tree-of-life index. Past work on compressed-index classification used matching statistics or pseudo-matching lengths to capture the fine-grained co-linearity of exact matches. But these fail to capture coarse-grained information about whether seeds appear co-linearly in the reference.

Sigmoni: classification of nanopore signal with a compressed pangenome index.

Summary: Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes.

Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.

Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic/transcriptomic and epigenetic information without additional library preparation. Presently, only a limited set of modifications can be directly basecalled (e.g.

Sigmoni: classification of nanopore signal with a compressed pangenome index.

Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes.

riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow.

Motivation: Ribosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses.

Results: We present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data.

Leaf Transcriptome Assembly of (Burseraceae) and Annotation of Terpene Biosynthetic Genes.

Plants in the Burseraceae are globally recognized for producing resins and essential oils with medicinal properties and have economic value. In addition, most of the aromatic and non-aromatic components of Burseraceae resins are derived from a variety of terpene and terpenoid chemicals. Although terpene genes have been identified in model plant crops (e.

SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants.

Background: Molecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis.

Transcriptome analysis of the curry tree (Bergera koenigii L., Rutaceae) during leaf development.

The curry tree (Bergera koenigii L.) is a widely cultivated plant used in South Asian cooking. Next-generation sequencing was used to generate the transcriptome of the curry leaf to detect changes in gene expression during leaf development, such as those genes involved in the production of oils which lend the leaf its characteristic taste, aroma, and medicinal properties.

Analysis of whole chloroplast genomes from the genera of the Clauseneae, the curry tribe (Rutaceae, Citrus family).

The Clauseneae (Aurantioideae, Rutaceae) is a tribe in the Citrus family that, although economically important as it contains the culinary and medicinally-useful curry tree (Bergera koenigii), has been relatively understudied. Due to the recent significant taxonomic changes made to this tribe, a closer inspection of the genetic relationships among its genera has been warranted. Whole genome skimming was used to generate chloroplast genomes from six species, representing each of the four genera (Bergera, Clausena, Glycosmis, Micromelum) in the Clauseneae tribe plus one closely related outgroup (Merrillia), using the published plastome sequence of Citrus sinensis as a reference.