Mohammadi, A. S. | Search bioRxiv

bioRxiv2024

Improving Cell-type-specific 3D Genome Architectures Prediction Leveraging Graph Neural Networks

The mammalian genome organizes into complex three-dimensional structures, where interactions among chromatin regulatory elements play a pivotal role in mediating biological functions, highlighting the significance of genomic region interactions in biological research. Traditional biological sequencing techniques like HiC and MicroC, commonly employed to estimate these interactions, are resource-intensive and time-consuming, especially given the vast array of cell lines and tissues involved. With the advent of advanced machine learning (ML) methodologies, there has been a push towards developing ML models to predict genomic interactions. However, while these models excel in predicting interactions for cell lines similar to their training data, they often fail to generalize across distantly related cell lines or accurately predict interactions specific to certain cell lines. Identifying the potential oversight of excluding example genomic region interaction information from model inputs as a fundamental limitation, this paper introduces GRACHIP, a model rooted in graph neural network technology aiming to address this issue by incorporating detailed interaction information as a hint. Through extensive testing across various cell lines, GRACHIP not only demonstrates exceptional accuracy in predicting chromatin interaction intensity but showcases remarkable generalizability to cell lines not encountered during training. Consequently, GRACHIP emerges as a potent research tool, offering a viable alternative to conventional sequencing methods for analyzing the interactions and three-dimensional organization of mammalian genomes, thus alleviating the dependency on expensive and time-consuming biological sequencing techniques. It also offers an alternative way for researchers to investigate 3D chromatin interactions and simulate their changes in model systems to test their hypotheses.

genomics↗

bioRxiv2023

Designing efficient randstrobes for sequence similarity analyses

Substrings of length k, commonly referred to as k-mers, play a vital role in sequence analysis, reducing the search space by providing anchors between queries and references. However, k-mers are limited to exact matches between sequences. This has led to alternative constructs, such as spaced k-mers, that can match across substitutions. We recently introduced a class of new constructs, strobemers, that can match across substitutions and smaller insertions and deletions. Randstrobes, the most sensitive strobemer proposed in [18], has been incorporated into several bioinformatics applications such as read classification, short read mapping, and read overlap detection. Randstrobes are constructed by linking together k-mers in a pseudo-random fashion and depend on a hash function, a link function, and a comparator for their construction. Recently, we showed that the more random this linking appears (measured in entropy), the more efficient the seeds for sequence similarity analysis. The level of pseudo-randomness will depend on the hashing, linking, and comparison operators. However, no study has investigated the efficacy of the underlying operators to produce randstrobes. In this study, we propose several new construction methods. One of our proposed methods is based on a Binary Search Tree (BST), which lowers the time complexity and practical runtime to other methods for some parametrizations. To our knowledge, we are also the first to describe and study the types of biases that occur during construction. We designed three metrics to measure the bias. Using these new evaluation metrics, we uncovered biases and limitations in previous methods and showed that our proposed methods have favorable speed and sampling uniformity to previously proposed methods. Lastly, guided by our results, we change the seed construction in strobealign, a short-read mapper, and find that the results change substantially. Also, we suggest combining the two versions to improve accuracy for the shortest reads in our evaluated datasets. Our evaluation highlights sampling biases that can occur and provides guidance on which operators to use when implementing randstrobes.

bioinformatics↗