Enhancing gene co-expression network inference for the malaria parasite<i>Plasmodium falciparum</i>
Abstract Background Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethalPlasmodium(P.) speciesP. falciparum. A fullP. falciparumgenome was published in 2002, yet 44.6% of its genes have unknown functions. Improving functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.Results Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks inP. falciparumhave been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for allP. falciparumgenes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out cross-validation. We assess overlaps of the different networks’ edges (gene co-expression relationships) as well as predicted functional knowledge. The networks’ edges are overall complementary: 47%-85% of all edges are unique to each network. In terms of accuracy of predicting gene functional annotations, all networks yield relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached is below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.Conclusions The different networks seem to capture different aspects of theP. falciparumbiology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.Supplementary data Attached.Availability and implementation All data and code are available at<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://nd.edu/~cone/pfalGCEN/">https://nd.edu/~cone/pfalGCEN/</jats:ext-link>.Contact <jats:email>tmilenkond.edu</jats:email>.
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 26. Dez. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Qi [VerfasserIn] |
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Volltext [kostenfrei] |
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| doi: |
10.1101/2023.05.31.543171 |
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| PPN (Katalog-ID): |
XBI03977614X |
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| 245 | 1 | 0 | |a Enhancing gene co-expression network inference for the malaria parasite<i>Plasmodium falciparum</i> |
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| 520 | |a Abstract Background Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethalPlasmodium(P.) speciesP. falciparum. A fullP. falciparumgenome was published in 2002, yet 44.6% of its genes have unknown functions. Improving functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.Results Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks inP. falciparumhave been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for allP. falciparumgenes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out cross-validation. We assess overlaps of the different networks’ edges (gene co-expression relationships) as well as predicted functional knowledge. The networks’ edges are overall complementary: 47%-85% of all edges are unique to each network. In terms of accuracy of predicting gene functional annotations, all networks yield relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached is below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.Conclusions The different networks seem to capture different aspects of theP. falciparumbiology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.Supplementary data Attached.Availability and implementation All data and code are available at<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://nd.edu/~cone/pfalGCEN/">https://nd.edu/~cone/pfalGCEN/</jats:ext-link>.Contact <jats:email>tmilenkond.edu</jats:email> | ||
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| 700 | 1 | |a Button-Simons, Katrina A |4 aut | |
| 700 | 1 | |a Sievert, Mackenzie AC |4 aut | |
| 700 | 1 | |a Chahoud, Elias |4 aut | |
| 700 | 1 | |a Foster, Gabriel F |4 aut | |
| 700 | 1 | |a Meis, Kaitlynn |4 aut | |
| 700 | 1 | |a Ferdig, Michael T |4 aut | |
| 700 | 1 | |a Milenković, Tijana |4 aut | |
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