Graph Positional and Structural Encoder

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches

Long Range Graph Benchmark

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.Comment: Added reference to T\"onshoff et al., 2023 in Sec. 4.1; NeurIPS 2022 Track on D&B; Open-sourced at: https://github.com/vijaydwivedi75/lrg

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature-viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability-signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/)

RNA motif search with data-driven element ordering

BACKGROUND: In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. RESULTS: We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. CONCLUSIONS: We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1074-x) contains supplementary material, which is available to authorized users

RNA motif search with data-driven element ordering

Abstract Background In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. Results We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. Conclusions We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo

RNA motif search with data-driven element ordering.

BackgroundIn this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms.ResultsWe have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools.ConclusionsWe have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo

Additional file 1 of RNA motif search with data-driven element ordering

Supplementary online material. The file contains supplementary material with additional details on methods, file formats, and experiments. (PDF 617 kb