Biology subjects

Migliorelli, G.

Publications and source records attributed to Migliorelli, G..

2 recordsLinked to original sources

bioRxiv2021

Chromosome-level genome assembly of the functionally extinct northern white rhinoceros (Ceratotherium simum cottoni).

The northern white rhinoceros (NWR; Ceratotherium simum cottoni) is functionally extinct, with only two females remaining alive. Efforts to rescue the NWR have inspired the exploration of unconventional conservation methods, including the generation of artificial gametes from induced pluripotent stem cells and somatic cell nuclear transfer. To enable the technologies required for these approaches, we used complementary sequencing and mapping methods to generate a NWR chromosome-level reference genome that meets or exceeds the metrics proposed by the Vertebrate Genome Project. It represents 40 autosomes, an X and a partially-resolved Y chromosome, and the mitochondrial genome. We compared the NWR reference genome to the southern white rhinoceros (SWR) population that has been physically separated from the NWR for tens of thousands of years. Using short-read data from the SWR and optical mapping, we found that the two populations are very similar on both the chromosome level and mitochondrial genome level. The results of this study are encouraging for the efforts underway to rescue the NWR.

genomics↗

bioRxiv2020

Global, Highly Specific and Fast Filtering of Alignment Seeds

An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only. We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity. An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks.

bioinformatics↗