DNA language models are powerful predictors of genome-wide variant effects.

The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN), a model designed to learn genome-wide variant effects through unsupervised pretraining on genomic DNA sequences. Our model also successfully learns gene structure and DNA motifs without any supervision. To demonstrate its utility, we train GPN on unaligned reference genomes of Arabidopsis thaliana and seven related species within the Brassicales order and evaluate its ability to predict the functional impact of genetic variants in A. thaliana by utilizing allele frequencies from the 1001 Genomes Project and a comprehensive database of GWAS. Notably, GPN outperforms predictors based on popular conservation scores such as phyloP and phastCons. Our predictions for A. thaliana can be visualized as sequence logos in the UCSC Genome Browser (https://genome.ucsc.edu/s/gbenegas/gpn-arabidopsis). We provide code (https://github.com/songlab-cal/gpn) to train GPN for any given species using its DNA sequence alone, enabling unsupervised prediction of variant effects across the entire genome.

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction.

Whereas protein language models have demonstrated remarkable efficacy in predicting the effects of missense variants, DNA counterparts have not yet achieved a similar competitive edge for genome-wide variant effect predictions, especially in complex genomes such as that of humans. To address this challenge, we here introduce GPN-MSA, a novel framework for DNA language models that leverages whole-genome sequence alignments across multiple species and takes only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC, OMIM), experimental functional assays (DMS, DepMap), and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and non-coding variants.

choros: correction of sequence-based biases for accurate quantification of ribosome profiling data.

Ribosome profiling quantifies translation genome-wide by sequencing ribosome-protected fragments, or footprints. Its single-codon resolution allows identification of translation regulation, such as ribosome stalls or pauses, on individual genes. However, enzyme preferences during library preparation lead to pervasive sequence artifacts that obscure translation dynamics. Widespread over- and under-representation of ribosome footprints can dominate local footprint densities and skew estimates of elongation rates by up to five fold. To address these biases and uncover true patterns of translation, we present choros, a computational method that models ribosome footprint distributions to provide bias-corrected footprint counts. choros uses negative binomial regression to accurately estimate two sets of parameters: (i) biological contributions from codon-specific translation elongation rates; and (ii) technical contributions from nuclease digestion and ligation efficiencies. We use these parameter estimates to generate bias correction factors that eliminate sequence artifacts. Applying choros to multiple ribosome profiling datasets, we are able to accurately quantify and attenuate ligation biases to provide more faithful measurements of ribosome distribution. We show that a pattern interpreted as pervasive ribosome pausing near the beginning of coding regions is likely to arise from technical biases. Incorporating choros into standard analysis pipelines will improve biological discovery from measurements of translation.

Robust and annotation-free analysis of alternative splicing across diverse cell types in mice.

Although alternative splicing is a fundamental and pervasive aspect of gene expression in higher eukaryotes, it is often omitted from single-cell studies due to quantification challenges inherent to commonly used short-read sequencing technologies. Here, we undertake the analysis of alternative splicing across numerous diverse murine cell types from two large-scale single-cell datasets-the Tabula Muris and BRAIN Initiative Cell Census Network-while accounting for understudied technical artifacts and unannotated events. We find strong and general cell-type-specific alternative splicing, complementary to total gene expression but of similar discriminatory value, and identify a large volume of novel splicing events. We specifically highlight splicing variation across different cell types in primary motor cortex neurons, bone marrow B cells, and various epithelial cells, and we show that the implicated transcripts include many genes which do not display total expression differences. To elucidate the regulation of alternative splicing, we build a custom predictive model based on splicing factor activity, recovering several known interactions while generating new hypotheses, including potential regulatory roles for novel alternative splicing events in critical genes like Khdrbs3 and Rbfox1. We make our results available using public interactive browsers to spur further exploration by the community.

Cells are the basic building blocks of all living things. There are numerous types of cells, and each cell has its own machinery to fulfill a specialised role. Despite their different purposes, most cells contain the same instructions, stored as DNA, on how to assemble the proteins needed to perform their intended functions. Cell types often vary in the frequency that each gene is read, leading to different quantities of proteins produced. Moreover, a process known as alternative splicing enables cells to build multiple proteins from the same gene. It works by joining fragments of a gene's code in various combinations. The resulting RNA sequences are molecular templates that cells use to assemble proteins. Analysing these RNA sequences reveals which genes are switched on in different tissues of the body, and what proteins are being made. However, despite recent advancements, alternative splicing is rarely studied in single cells because of some sizeable technical challenges. Benegas, Fischer and Song developed a computational toolkit designed to handle the unique challenges of analysing alternative splicing events in single cells. The analysis pipeline, called scQuint, was tested on two large datasets that capture cell-to-cell differences in the brain and other tissues of mice. Nearly all the cell types studied exhibited clear differences in alternative splicing, such that cell types could be distinguished based on their splicing profiles. Intriguing patterns of splicing were highlighted in some immune cells and certain types of neurons. Across cell types, the genes with unique splicing patterns were often not the same as those with unique activity patterns, indicating that gene expression and alternative splicing are two complementary processes. New types of alternative splicing events were also identified. Benegas et al. also developed a statistical model to probe the roles of splicing regulators in different cell types. In summary, the scQuint toolkit overcomes critical technical challenges typically encountered when analysing alternative splicing in single cells. It also reveals new insights about mechanisms of alternative splicing. The results are open access, made available using public interactive browsers, which should spur on other researchers to interrogate how alternative splicing differs in single cells.