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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

Gene Duplication and Divergence

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

Alternative RNA Splicing

Alternative RNA Splicing

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DASSI: differential architecture search for splice identification from DNA sequences.

Shabir Moosa^1,2, Prof Abbes Amira³, Dr Sabri Boughorbel⁴

¹Department of Systems Biology, SIDRA Medicine, Doha, 26999, Qatar. smoosa@sidra.org.

|February 16, 2021

Summary

We developed DASSI, an automated method for discovering high-performance deep learning models for genomics tasks like splice site recognition. This approach efficiently finds novel architectures, outperforming existing methods.

Keywords:

Convolutional neural networks Deep learning Genomics Neural architecture search Splice site

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Genomic data interpretation faces challenges due to rapid data growth.
Deep Learning (DL) has advanced various fields but requires specialized models for genomics.
Automated DL architecture design is crucial for handling complex genomic data.

Purpose of the Study:

To develop an automated method for discovering high-performance DL architectures for genomic sequence analysis.
To apply differential architecture search to the Splice Site (SS) recognition task.
To discover novel, efficient convolutional neural network architectures for genomics.

Main Methods:

Adapted a differential architecture search method to create the DASSI model.
Applied DASSI to automated discovery of convolutional architectures for DNA sequences.
Evaluated DASSI against state-of-the-art tools for Splice Site classification across multiple species.

Main Results:

The discovered DASSI architecture outperformed baseline and fixed models in SS classification.
DASSI demonstrated competitive performance against state-of-the-art methods.
Benchmarking showed DASSI's computational efficiency on modern GPUs, enabling large-scale application.

Conclusions:

Differential architecture search (DASSI) effectively performs SS classification on raw DNA.
New neural network models with fewer parameters and competitive performance were discovered.
Automated architecture search shows significant potential for diverse genomics problems.