In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds
Background High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-07-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/11691.pdf |
| _version_ | 1827607269994397696 |
|---|---|
| author | Consolata Gakii Billiah Kemunto Bwana Grace Gathoni Mugambi Esther Mukoya Paul O. Mireji Richard Rimiru |
| author_facet | Consolata Gakii Billiah Kemunto Bwana Grace Gathoni Mugambi Esther Mukoya Paul O. Mireji Richard Rimiru |
| author_sort | Consolata Gakii |
| collection | DOAJ |
| description | Background High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem. Methodology In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes. Results and discussion Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent. |
| first_indexed | 2024-03-09T06:52:04Z |
| format | Article |
| id | doaj.art-6791252c3b1943cbaf098f8a2e0189c5 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:52:04Z |
| publishDate | 2021-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-6791252c3b1943cbaf098f8a2e0189c52023-12-03T10:24:24ZengPeerJ Inc.PeerJ2167-83592021-07-019e1169110.7717/peerj.11691In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compoundsConsolata Gakii0Billiah Kemunto Bwana1Grace Gathoni Mugambi2Esther Mukoya3Paul O. Mireji4Richard Rimiru5Department of Mathematics, Computing and Information Technology, University of Embu, Embu, Eastern, KenyaDepartment of Biological Sciences, University of Embu, Embu, Eastern, KenyaSchool of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Nairobi, KenyaSchool of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Nairobi, KenyaBiotechnology Research Center, Kenya Agricultural & Livestock Research Organization, Nairobi, Nairobi, KenyaSchool of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Nairobi, KenyaBackground High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem. Methodology In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes. Results and discussion Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent.https://peerj.com/articles/11691.pdfAssociation rule miningCo-expression networkRNASeq dataDiscretizationIn silico analysis |
| spellingShingle | Consolata Gakii Billiah Kemunto Bwana Grace Gathoni Mugambi Esther Mukoya Paul O. Mireji Richard Rimiru In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds PeerJ Association rule mining Co-expression network RNASeq data Discretization In silico analysis |
| title | In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| title_full | In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| title_fullStr | In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| title_full_unstemmed | In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| title_short | In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| title_sort | in silico driven analysis of the glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds |
| topic | Association rule mining Co-expression network RNASeq data Discretization In silico analysis |
| url | https://peerj.com/articles/11691.pdf |
| work_keys_str_mv | AT consolatagakii insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds AT billiahkemuntobwana insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds AT gracegathonimugambi insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds AT esthermukoya insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds AT paulomireji insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds AT richardrimiru insilicodrivenanalysisoftheglossinamorsitansmorsitansantennaetranscriptomeinresponsetorepellentorattractantcompounds |