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Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...

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Related Experiment Video

Updated: Aug 8, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics.

Jiaqiang Zhu^1,2, Lulu Shang^1,2, Xiang Zhou^3,4

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.

|March 3, 2023

Summary

This summary is machine-generated.

SRTsim generates realistic simulated spatially resolved transcriptomics data, addressing limitations of existing methods. This tool enables reproducible benchmarking of computational approaches for spatial analysis.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Spatially resolved transcriptomics (SRT) analysis relies on computational methods often validated with simulated data.
Existing simulators lack spatial information, are poorly documented, or produce unrealistic data.

Purpose of the Study:

To introduce SRTsim, a novel simulator for scalable, reproducible, and realistic SRT data.
To overcome the limitations of current in silico methods for SRT data generation.

Main Methods:

SRTsim is designed specifically for SRT data simulation.
It preserves key expression characteristics and spatial patterns inherent to SRT data.

Main Results:

SRTsim facilitates the creation of realistic simulated SRT datasets.
Demonstrated utility in benchmarking spatial clustering, pattern detection, and cell-cell communication identification methods.

Conclusions:

SRTsim provides a valuable tool for developing and validating SRT computational methods.
Enhances the reliability and reproducibility of SRT data analysis pipelines.