Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination
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2018
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Primary goals of 21st century science involve eco-friendly solutions for detection, control and suppression of plant viruses. Even though we are accumulating knowledge and data on plant viruses' nucleotide sequences, we are still using a minimum of information available from the collected data. Applying bioinformatics tools and data mining approach to viral sequences is extremely useful in revealing the hidden knowledge, giving guidelines for further biological/bioinformatics studies and developing novel environmental-friendly virus specific defense strategies in crop protection. In this paper we tested to what extent modern bioinformatics methods are able to reveal new information that would bring us closer to our primary goals. On the date of the search (March 2015) we extracted all available PMMoV entries from publically available databases, represented by heterogeneous data set containing 231 nucleotide sequences covering different parts of the PMMoV genome, that were of different ...geographical origin, related to different time periods, associated with different pathotypes, and were not previously compared to each other. Results revealed that nucleotide content at genomic positions 552, 565, 639, 666, 708, 5921, 5975 and 6002 can be used to discern three distinct PMMoV genotype variants and their association to one of two virus pathotypes, P-1,P-2 or P-1,P-2,P-3. These sites have never been reported as informative before, probably because by being silent mutations they escaped usual research scrutiny of looking for pathotype determinants among nonsense, missense mutations and indels. Our model was further tested in predicting pathotype of ten newly deposited PMMoV sequences and the successful outcome of the test supported the model as an useful asset for discrimination among pathotypes P-1,P-2 and P-1,P-2,P-3 according to distinct nucleotide content in replicase and coat protein encoding genes. Based on the presented results, we also suggested new tests for fast and cost-effective screening of PMMoV pathotypes and eventually for inducing plant resistance against pepper mild mottle virus.
Ključne reči:
PMMoV / Pepper mild mottle virus / Pathotype / Monitoring / Data mining / CapsicumIzvor:
Crop Protection, 2018, 107, 93-103Izdavač:
- Elsevier Sci Ltd, Oxford
Finansiranje / projekti:
- Molekularni mehanizmi odgovora biljaka na abiotički stres-uloga transkripcionih faktora i malih RNK i analiza genetičkog diverziteta biljnih kultura od interesa za poljoprivredu i biotehnologiju (RS-MESTD-Basic Research (BR or ON)-173005)
DOI: 10.1016/j.cropro.2018.01.017
ISSN: 0261-2194
WoS: 000427209000013
Scopus: 2-s2.0-85041404143
Institucija/grupa
Institut za molekularnu genetiku i genetičko inženjerstvoTY - JOUR AU - Banović Đeri, Bojana AU - Pajić, Vesna AU - Dudić, Dragana PY - 2018 UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1191 AB - Primary goals of 21st century science involve eco-friendly solutions for detection, control and suppression of plant viruses. Even though we are accumulating knowledge and data on plant viruses' nucleotide sequences, we are still using a minimum of information available from the collected data. Applying bioinformatics tools and data mining approach to viral sequences is extremely useful in revealing the hidden knowledge, giving guidelines for further biological/bioinformatics studies and developing novel environmental-friendly virus specific defense strategies in crop protection. In this paper we tested to what extent modern bioinformatics methods are able to reveal new information that would bring us closer to our primary goals. On the date of the search (March 2015) we extracted all available PMMoV entries from publically available databases, represented by heterogeneous data set containing 231 nucleotide sequences covering different parts of the PMMoV genome, that were of different geographical origin, related to different time periods, associated with different pathotypes, and were not previously compared to each other. Results revealed that nucleotide content at genomic positions 552, 565, 639, 666, 708, 5921, 5975 and 6002 can be used to discern three distinct PMMoV genotype variants and their association to one of two virus pathotypes, P-1,P-2 or P-1,P-2,P-3. These sites have never been reported as informative before, probably because by being silent mutations they escaped usual research scrutiny of looking for pathotype determinants among nonsense, missense mutations and indels. Our model was further tested in predicting pathotype of ten newly deposited PMMoV sequences and the successful outcome of the test supported the model as an useful asset for discrimination among pathotypes P-1,P-2 and P-1,P-2,P-3 according to distinct nucleotide content in replicase and coat protein encoding genes. Based on the presented results, we also suggested new tests for fast and cost-effective screening of PMMoV pathotypes and eventually for inducing plant resistance against pepper mild mottle virus. PB - Elsevier Sci Ltd, Oxford T2 - Crop Protection T1 - Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination EP - 103 SP - 93 VL - 107 DO - 10.1016/j.cropro.2018.01.017 ER -
@article{ author = "Banović Đeri, Bojana and Pajić, Vesna and Dudić, Dragana", year = "2018", abstract = "Primary goals of 21st century science involve eco-friendly solutions for detection, control and suppression of plant viruses. Even though we are accumulating knowledge and data on plant viruses' nucleotide sequences, we are still using a minimum of information available from the collected data. Applying bioinformatics tools and data mining approach to viral sequences is extremely useful in revealing the hidden knowledge, giving guidelines for further biological/bioinformatics studies and developing novel environmental-friendly virus specific defense strategies in crop protection. In this paper we tested to what extent modern bioinformatics methods are able to reveal new information that would bring us closer to our primary goals. On the date of the search (March 2015) we extracted all available PMMoV entries from publically available databases, represented by heterogeneous data set containing 231 nucleotide sequences covering different parts of the PMMoV genome, that were of different geographical origin, related to different time periods, associated with different pathotypes, and were not previously compared to each other. Results revealed that nucleotide content at genomic positions 552, 565, 639, 666, 708, 5921, 5975 and 6002 can be used to discern three distinct PMMoV genotype variants and their association to one of two virus pathotypes, P-1,P-2 or P-1,P-2,P-3. These sites have never been reported as informative before, probably because by being silent mutations they escaped usual research scrutiny of looking for pathotype determinants among nonsense, missense mutations and indels. Our model was further tested in predicting pathotype of ten newly deposited PMMoV sequences and the successful outcome of the test supported the model as an useful asset for discrimination among pathotypes P-1,P-2 and P-1,P-2,P-3 according to distinct nucleotide content in replicase and coat protein encoding genes. Based on the presented results, we also suggested new tests for fast and cost-effective screening of PMMoV pathotypes and eventually for inducing plant resistance against pepper mild mottle virus.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Crop Protection", title = "Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination", pages = "103-93", volume = "107", doi = "10.1016/j.cropro.2018.01.017" }
Banović Đeri, B., Pajić, V.,& Dudić, D.. (2018). Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination. in Crop Protection Elsevier Sci Ltd, Oxford., 107, 93-103. https://doi.org/10.1016/j.cropro.2018.01.017
Banović Đeri B, Pajić V, Dudić D. Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination. in Crop Protection. 2018;107:93-103. doi:10.1016/j.cropro.2018.01.017 .
Banović Đeri, Bojana, Pajić, Vesna, Dudić, Dragana, "Revealing new information from existing genomic data for pepper mild mottle virus pathotype determination" in Crop Protection, 107 (2018):93-103, https://doi.org/10.1016/j.cropro.2018.01.017 . .