GeneWalk identifies relevant gene functions for a biological context using network representation learning
Posted on: 4 October 2019
Preprint posted on 5 September 2019
Article now published in Genome Biology at http://dx.doi.org/10.1186/s13059-021-02264-8
Fear The Walking Gene: knowledge-based machine learning is able to highlight gene functions relevant for a distinct biological context
Selected by Ramona Jühlen
Background and GeneWalk methodology
High-throughput functional genomics can provide scientists with a long list of candidate genes which could play a role in the biological context they are studying. But how should one narrow down such a list to get candidate genes which are the most important in the given context? Gene functions are commonly interrogated using GO annotations, and GO coupled to gene set enrichment analysis (GSEA) can be used to reveal enriched biological functions in a gene set. This analysis, however, does not address the context-specific functions of individual genes in the dataset. To overcome this shortcoming, the authors developed GeneWalk, a novel approach using knowledge-based machine learning and statistical modelling.
First, GeneWalk assembles a context-specific gene network from a knowledge base (e.g. Pathway Commons, INDRA) starting with a list of input genes obtained from a specific experiment. This gene network is added to a GO network resulting in a full GeneWalk network (GWN) (Figure 1).
Next, the GWN structure is learned by an unsupervised network representation learning algorithm, termed DeepWalk (1). Briefly, using random walks the local neighbourhood of nodes (representing genes or GO terms) is scanned, summarised as a collection of neighbouring node pairs and provided as a training set for a neural network with one hidden layer (the layer between input and out, i.e. the artificial neuron) (Figure 1). After training, each input node in the GWN is represented as a vector by the resultant hidden layer weights.
Finally, GeneWalk determines by significance testing whether the similarity value between a gene and a GO term is higher than that of a generated null distribution of similarity values (Figure 1). Yielded adjusted p-values rank the relevant context-specific GO term for a gene of interest.
Figure 1. Scheme of GeneWalk methodology. Details outlining GeneWalk network representation learning and significance testing of the GeneWalk methodology.
Example applications of GeneWalk
To test GeneWalk the authors set out to use it first in an already characterised experimental context. Oligodendrocytes myelinate neurons in the brain in a QKI-dependent mechanism, where the gene QKI codes for a RNA-binding protein involved in alternative splicing. RNA-sequencing data of QkI-deficient murine oligodendrocytes revealed 1899 differentially expressed genes, and several of those strong down-regulated genes have been linked to neuron myelination (e.g. Mal, Pllp, Plp1) (2). GeneWalk, using the knowledge base INDRA, identified in the RNA-sequencing data of QkI-deficient murine brains that GO terms linked to neuron myelination were most similar to the differentially expressed genes Mal, Pllp and Plp1. GSEA analysis using PANTHER also identified myelination-related processes to be enriched; however, specific gene functions in this biological context could not be recovered. Additionally, the authors present that GeneWalk is not influenced by biases from genes with a high or low number of GO annotations, or from the degree of connectivity of GO annotations of a gene
Next, in order to apply GeneWalk in a different experimental set-up the authors reanalysed
published Native Elongation Transcript sequencing (NET-seq) data of a human T-cell acute
lymphoblastic leukaemia (ALL) cell line responding to treatment with JQ1 (3). JQ1 is a small drug
that targets BRD4 and other BET family members that are involved in haematologic cancers like
ALL. With NET-seq a quantitative read-out of the nascent transcription is possible. By first
calculating differentially transcribed protein-coding genes, GeneWalk identified 28% similar GO
terms for these genes, whereas conventional GSEA only identified five high-level functions with
low fold enrichment. These results reveal the advantage of GeneWalk (and disadvantage of GSEA),
when a magnitude of functionally unrelated genes are mis-regulated. Furthermore, in this
experiment GeneWalk was able to systematically prioritise context-specific functions of genes with
a multitude of GO annotations (e.g. MYC or BRCA1), that are not all relevant for this specific
biological context.
As a third application of GeneWalk the authors generated NET-seq data from HeLa cells treated
with the biflavonoid isoginkgetin (IsoG). IsoG is a plant-derived compound with possible anticancerogenic abilities. It has been shown that IsoG inhibits pre-mRNA splicing in vitro and in vivo
and causes Pol II accumulation at the 5’-end of genes (4); however, its exact mode of action
remains to be elucidated. NET-seq revealed 2940 genes as differentially transcribed upon IsoG
treatment and GeneWalk found that 24% of these genes had at least one similar GO term. On the
contrast to GSEA, GeneWalk found HES1, EGR1 and IRF1 as plausible candidate genes for
inhibiting Pol II transcriptional elongation after IsoG treatment.
Summed up, the authors provide a novel computational tool that is able to identify context-specific
gene functions in gene sets of experimental assays. These assays are not limited to input data of
RNA-sequencing or NET-seq, but can also be transferred to e.g. CRISPR screens or mass
spectrometry approaches.
What I like about this work and open questions
GeneWalk supplements over-representation tests and GSEA of GO annotations. I am currently
doing GSEA using the R package clusterProfiler (5), and now I will alternatively analyse my data
using GeneWalk in order to complement my results. Both tools seem to be a great combination
(they are also both open source)!
It will be good to know whether it will be possible in the future to add another genome wide
annotation parameter by mapping Entrez Gene identifiers, so that data of more species can be
analysed (Bioconductor provides OrgDb for 20 species).
Additional references
1. B. Perozzi, R. Al-Rfou, S. Skiena, Proceedings of the 20th ACM SIGKDD international
conference on Knowledge discovery and data mining – KDD ’14, 701–710 (2014).
2. L. Darbelli, K. Choquet, S. Richard, C. L. Kleinman, Sci Rep. 7, 1–13 (2017).
3. G. E. Winter et al., Mol. Cell. 67, 5-18.e19 (2017).
4. K. O’Brien, A. J. Matlin, A. M. Lowell, M. J. Moore, J. Biol. Chem. 283, 33147–33154 (2008).
5. G. Yu, L.-G. Wang, Y. Han, Q.-Y. He, OMICS. 16, 284–287 (2012).
More information
https://github.com/churchmanlab/genewalk
https://churchman.med.harvard.edu/genewalk
doi: https://doi.org/10.1242/prelights.14324
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BSCB-Biochemical Society 2024 Cell Migration meeting
This preList features preprints that were discussed and presented during the BSCB-Biochemical Society 2024 Cell Migration meeting in Birmingham, UK in April 2024. Kindly put together by Sara Morais da Silva, Reviews Editor at Journal of Cell Science.
| List by | Reinier Prosee |
9th International Symposium on the Biology of Vertebrate Sex Determination
This preList contains preprints discussed during the 9th International Symposium on the Biology of Vertebrate Sex Determination. This conference was held in Kona, Hawaii from April 17th to 21st 2023.
| List by | Martin Estermann |
Semmelweis Symposium 2022: 40th anniversary of international medical education at Semmelweis University
This preList contains preprints discussed during the 'Semmelweis Symposium 2022' (7-9 November), organised around the 40th anniversary of international medical education at Semmelweis University covering a wide range of topics.
| List by | Nándor Lipták |
20th “Genetics Workshops in Hungary”, Szeged (25th, September)
In this annual conference, Hungarian geneticists, biochemists and biotechnologists presented their works. Link: http://group.szbk.u-szeged.hu/minikonf/archive/prg2021.pdf
| List by | Nándor Lipták |
EMBL Conference: From functional genomics to systems biology
Preprints presented at the virtual EMBL conference "from functional genomics and systems biology", 16-19 November 2020
| List by | Jesus Victorino |
TAGC 2020
Preprints recently presented at the virtual Allied Genetics Conference, April 22-26, 2020. #TAGC20
| List by | Maiko Kitaoka et al. |
Zebrafish immunology
A compilation of cutting-edge research that uses the zebrafish as a model system to elucidate novel immunological mechanisms in health and disease.
| List by | Shikha Nayar |
Also in the molecular biology category:
2024 Hypothalamus GRC
This 2024 Hypothalamus GRC (Gordon Research Conference) preList offers an overview of cutting-edge research focused on the hypothalamus, a critical brain region involved in regulating homeostasis, behavior, and neuroendocrine functions. The studies included cover a range of topics, including neural circuits, molecular mechanisms, and the role of the hypothalamus in health and disease. This collection highlights some of the latest advances in understanding hypothalamic function, with potential implications for treating disorders such as obesity, stress, and metabolic diseases.
| List by | Nathalie Krauth |
BSCB-Biochemical Society 2024 Cell Migration meeting
This preList features preprints that were discussed and presented during the BSCB-Biochemical Society 2024 Cell Migration meeting in Birmingham, UK in April 2024. Kindly put together by Sara Morais da Silva, Reviews Editor at Journal of Cell Science.
| List by | Reinier Prosee |
‘In preprints’ from Development 2022-2023
A list of the preprints featured in Development's 'In preprints' articles between 2022-2023
| List by | Alex Eve, Katherine Brown |
CSHL 87th Symposium: Stem Cells
Preprints mentioned by speakers at the #CSHLsymp23
| List by | Alex Eve |
9th International Symposium on the Biology of Vertebrate Sex Determination
This preList contains preprints discussed during the 9th International Symposium on the Biology of Vertebrate Sex Determination. This conference was held in Kona, Hawaii from April 17th to 21st 2023.
| List by | Martin Estermann |
Alumni picks – preLights 5th Birthday
This preList contains preprints that were picked and highlighted by preLights Alumni - an initiative that was set up to mark preLights 5th birthday. More entries will follow throughout February and March 2023.
| List by | Sergio Menchero et al. |
CellBio 2022 – An ASCB/EMBO Meeting
This preLists features preprints that were discussed and presented during the CellBio 2022 meeting in Washington, DC in December 2022.
| List by | Nadja Hümpfer et al. |
EMBL Synthetic Morphogenesis: From Gene Circuits to Tissue Architecture (2021)
A list of preprints mentioned at the #EESmorphoG virtual meeting in 2021.
| List by | Alex Eve |
FENS 2020
A collection of preprints presented during the virtual meeting of the Federation of European Neuroscience Societies (FENS) in 2020
| List by | Ana Dorrego-Rivas |
ECFG15 – Fungal biology
Preprints presented at 15th European Conference on Fungal Genetics 17-20 February 2020 Rome
| List by | Hiral Shah |
ASCB EMBO Annual Meeting 2019
A collection of preprints presented at the 2019 ASCB EMBO Meeting in Washington, DC (December 7-11)
| List by | Madhuja Samaddar et al. |
Lung Disease and Regeneration
This preprint list compiles highlights from the field of lung biology.
| List by | Rob Hynds |
MitoList
This list of preprints is focused on work expanding our knowledge on mitochondria in any organism, tissue or cell type, from the normal biology to the pathology.
| List by | Sandra Franco Iborra |