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Biology subjects

Amarasinghe, S.

Publications and source records attributed to Amarasinghe, S..

2 recordsLinked to original sources

Compensatory growth and recovery of tissue cytoarchitecture after transient cartilage-specific cell death in foetal mouse limbs

A major question in developmental and regenerative biology is how organ size is controlled by progenitor cells. For example, while limb bones exhibit catch-up growth (recovery of a normal growth trajectory after transient developmental perturbation), it is unclear how this emerges from the behaviour of chondroprogenitors, the cells sustaining the cartilage anlagen that are progressively replaced by bone. Here we show that transient sparse cell death in the mouse foetal cartilage was repaired postnatally, via a two-step process. During injury, progression of chondroprogenitors towards more differentiated states was delayed, leading to altered cartilage cytoarchitecture and impaired bone growth. Then, once cell death was over, chondroprogenitor differentiation was accelerated and cartilage structure recovered, including partial rescue of bone growth. At the molecular level, ectopic activation of mTORC1 correlated with, and was necessary for, part of the recovery, revealing a specific candidate to be explored during normal growth and in future therapies.

developmental biology↗

A Python-based optimization framework for high-performance genomics

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq--a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

bioinformatics↗