Horizontally Transferred Genetic Elements in the Tsetse Fly Genome: An Alignment-Free Clustering Approach Using Batch Learning Self-Organising Map (BLSOM)

Abstract

Tsetse flies (Glossina spp.) are the primary vectors of trypanosomes, which can cause human and animal African trypanosomiasis in Sub-Saharan African countries. The objective of this study was to explore the genome of Glossina morsitans morsitans for evidence of horizontal gene transfer (HGT) from microorganisms. We employed an alignment-free clustering method, that is, batch learning self-organising map (BLSOM), in which sequence fragments are clustered based on the similarity of oligonucleotide frequencies independently of sequence homology. After an initial scan of HGT events using BLSOM, we identified 3.8% of the tsetse fly genome as HGT candidates. The predicted donors of these HGT candidates included known symbionts, such as Wolbachia, as well as bacteria that have not previously been associated with the tsetse fly. We detected HGT candidates from diverse bacteria such as Bacillus and Flavobacteria, suggesting a past association between these taxa. Functional annotation revealed that the HGT candidates encoded loci in various functional pathways, such as metabolic and antibiotic biosynthesis pathways. These findings provide a basis for understanding the coevolutionary history of the tsetse fly and its microbes and establish the effectiveness of BLSOM for the detection of HGT events.

Abbreviations and acronyms

“…LGT of fragments of the Wolbachia genome (total size approximately 1.2 Mb), ranging from 500 base pairs to more than 1 Mb, have been observed in many invertebrates, including beetles (Nikoh et al, 2008), grasshoppers (Funkhouser-Jones, 2015;Toribio-Fernández et al, 2017), wasps (Dunning-Hotopp et al, 2007), fruit flies (Dunning-Hotopp et al, 2007;Klasson et al, 2014;Choi, Bubnell and Aquadro, 2015;Morrow et al, 2015), tsetse flies (Brelsfoard et al, 2014;Nakao et al, 2016), butterflies and moths , kissing bugs (Mesquita et al, 2015), mosquitoes (Klasson et al, 2009;Hou et al, 2014), filarial nematodes (Fenn et al, 2006;Dunning-Hotopp et al, 2007;Keroack et al, 2016) and spiders (Baldo et al, 2008).…”

Foreword

“…LGT of fragments of the Wolbachia genome (total size approximately 1.2 Mb), ranging from 500 base pairs to more than 1 Mb, have been observed in many invertebrates, including beetles (Nikoh et al, 2008), grasshoppers (Funkhouser-Jones, 2015Toribio-Fernández et al, 2017), wasps (Dunning-Hotopp et al, 2007), fruit flies (Dunning-Hotopp et al, 2007;Klasson et al, 2014;Choi, Bubnell and Aquadro, 2015;Morrow et al, 2015), tsetse flies (Brelsfoard et al, 2014;Nakao et al, 2016), butterflies and moths (Ahmed et al, 2016), kissing bugs (Mesquita et al, 2015), mosquitoes (Klasson et al, 2009;Hou et al, 2014), filarial nematodes (Fenn et al, 2006;Dunning-Hotopp et al, 2007;Keroack et al, 2016) and spiders (Baldo et al, 2008).…”

Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model

“…The ordinary competitive learning network is enough to deal with the representative cluster problem,however, it is hard for it to deal with the huge input data set, the SOFM can overcome this shortcoming, [21] besides, making use of the topological structure of SOM, the relation between data can be clearly visualized. [22] A SOM maps the neurons upon one matrix of 2-dimensional or 3dimensional generally just like fig 6.…”

“…GetResponse(X) The coalitions are short-lived and goal-oriented [6].If a coalition lasts longer than the default requirement lif e, it will be considered as a failure. The algorithms shown in Algorithm 3 are explained in the following manner:The organizer initializes the neuron network(01).then the pursuers start to catch evaders under the coordination of organizers until all evaders are captured(02-33).The organizer broadcast position and value of each evader found in environment and wait the response from pursuers(03-06).The pursuers response with their own feature vector − → x p (07-09).The organizer train the SOM layer with the set of feature vectors X and send target and − −− → GAF to each pursuer after creating group p (10)(11)(12)(13)(14).The pursuer p response their CEF (p) and start get close to its target (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).The organizer train its feature extraction part with − −− → CEF (31-32).The sequences diagram describing orginaser and pursuer communications is shown in Figure13.…”