Biology subjects

Wygoda, E.

Publications and source records attributed to Wygoda, E..

3 recordsLinked to original sources

bioRxiv2024

BetaAlign: a deep learning approach for multiple sequence alignment

The multiple sequence alignment (MSA) problem is a fundamental pillar in bioinformatics, comparative genomics, and phylogenetics. Here we characterize and improve BetaAlign, the first deep learning aligner, which substantially deviates from conventional algorithms of alignment computation. BetaAlign draws on natural language processing (NLP) techniques and trains transformers to map a set of unaligned biological sequences to an MSA. We show that our approach is highly accurate, comparable and sometimes better than state-of-the-art alignment tools. We characterize the performance of BetaAlign and the effect of various aspects on accuracy; for example, the size of the training data, the effect of different transformer architectures, and the effect of learning on a subspace of indel-model parameters (subspace learning). We also introduce a new technique that leads to improved performance compared to our previous approach. Our findings further uncover the potential of NLP-based approaches for sequence alignment, highlighting that AI-based methodologies can substantially challenge classic tasks in phylogenomics and bioinformatics.

bioinformatics↗

bioRxiv2022

The evolutionary dynamics that retain long neutral genomic sequences in face of indel deletion bias: a model and its application to human introns

Insertions and deletions (indels) of short DNA segments are common evolutionary events. Numerous studies showed that deletions occur more often than insertions in both prokaryotes and eukaryotes. It raises the question why neutral sequences are not eradicated from the genome. We suggest that this is due to a phenomenon we term border-induced selection. Accordingly, a neutral sequence is bordered between conserved regions. Deletions occurring near the borders occasionally protrude to the conserved region and are thereby subject to strong purifying selection. Thus, for short neutral sequences, an insertion bias is expected. Here, we develop a set of increasingly complex models of indel-dynamics that incorporate border-induced selection. Furthermore, we show that short conserved sequences within the neutrally evolving sequence help explain: (1) the presence of very long sequences; (2) the high variance of sequence lengths; (3) the possible emergence of multimodality in sequence length distributions. Finally, we fitted our models to the human intron length distribution, as introns are thought to be mostly neutral and bordered by conserved exons. We show that when accounting for the occurrence of short conserved sequences within introns, we reproduce the main features, including the presence of long introns and the multimodality of intron distribution.

bioinformatics↗

bioRxiv2022

Harnessing machine translation methods for sequence alignment

The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a novel methodology for aligning sequences using a natural language processing (NLP) approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to outstanding alignment accuracy, often outperforming commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, and MUSCLE. Notably, the utilization of deep-learning techniques for the sequence alignment problem brings additional advantages, such as automatic feature extraction that can be leveraged for a variety of downstream analysis tasks.

evolutionary biology↗