The genome atlas: navigating a new era of reference genomes.

The reference genome serves two distinct purposes within the field of genomics. First, it provides a persistent structure against which findings can be reported, allowing for universal knowledge exchange between users. Second, it reduces the computational costs and time required to process genomic data by creating a scaffold that can be relied upon by analysis software. Here, we posit that current efforts to extend the linear reference to a graph-based structure while trying to fulfil both of these purposes concurrently will face a trade-off between comprehensiveness and computational efficiency. In this article, we explore how the reference genome is used and suggest an alternative structure, The Genome Atlas (TGA), to fulfil the bipartite role of the reference genome.

The identification of cis-regulatory elements: A review from a machine learning perspective.

The majority of the human genome consists of non-coding regions that have been called junk DNA. However, recent studies have unveiled that these regions contain cis-regulatory elements, such as promoters, enhancers, silencers, insulators, etc. These regulatory elements can play crucial roles in controlling gene expressions in specific cell types, conditions, and developmental stages. Disruption to these regions could contribute to phenotype changes. Precisely identifying regulatory elements is key to deciphering the mechanisms underlying transcriptional regulation. Cis-regulatory events are complex processes that involve chromatin accessibility, transcription factor binding, DNA methylation, histone modifications, and the interactions between them. The development of next-generation sequencing techniques has allowed us to capture these genomic features in depth. Applied analysis of genome sequences for clinical genetics has increased the urgency for detecting these regions. However, the complexity of cis-regulatory events and the deluge of sequencing data require accurate and efficient computational approaches, in particular, machine learning techniques. In this review, we describe machine learning approaches for predicting transcription factor binding sites, enhancers, and promoters, primarily driven by next-generation sequencing data. Data sources are provided in order to facilitate testing of novel methods. The purpose of this review is to attract computational experts and data scientists to advance this field.