Early Cretaceous lepidosaur (sphenodontian?) burrows.

Scarce fossil tetrapod burrows have been recorded in Cretaceous rocks, which is probably linked to the dominant equable climates that existed for most of this period. The occurrence of Cretaceous tetrapod burrows from Patagonia (Chubut Province, Argentina) dated between 118 and 115 million years ago, gives insights into their paleoecology and paleoenvironment. The rocks containing the tetrapod burrows are of pyroclastic origin and represent eolian dunes and ash-fall deposits, some reworked by fluvial currents and others showing soil development.

HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization.

Background: Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification.

A new parallel pipeline for DNA methylation analysis of long reads datasets.

Background: DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years.

A parallel and sensitive software tool for methylation analysis on multicore platforms.

Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed.