Open Access
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
Article Number 01007
Number of page(s) 5
Section Sustainable Infrastucture, Industry, Architecture, and Food Technology
Published online 17 May 2023
  1. F. Garcia-Pichel, J. Belnap, S. Neuer, F. Schanz, Estimates of global cyanobacterial biomass and its distribution, Algological Studies 109, pp. 213–227 (2003) [CrossRef] [Google Scholar]
  2. A. D. Jungblut, C. Lovejoy, W. F. Vincent, Global distribution of cyanobacterial ecotypes in the cold biosphere, The ISME J. 4, pp. 191–202 (2010) [CrossRef] [PubMed] [Google Scholar]
  3. P. Flombaum, J. L. Gallegos, R. A. Gordillo, J. Rincón, L. L. Zabala, N. Jiao, D. M. Karl, W. K. W. Li, M. W. Lomas, D. Veneziano, C. S. Vera, J. A. Vrugt, A. C. Martiny, Present and future global distributions of the marine cyanobacteria prochlorococcus and synechococcus, Proceedings of the National Academy of Sciences of the United States of America 110, 24, pp. 9824–9829 (2013) [CrossRef] [PubMed] [Google Scholar]
  4. B. E. Schirrmeister, A. Antonelli, H. C. Bagheri, The origin of multicellularity in cyanobacteria, BMC Evolutionary Biology 11, 1, pp. 45 (2011) [CrossRef] [PubMed] [Google Scholar]
  5. J. S. Singh, A. Kumar, A. N. Rai, D. P. Singh, Cyanobacteria : a precious bio-resource in agriculture, ecosystem, and environmental sustainability, Frontiers in Microbiology 7, pp. 1–19 (2016) [PubMed] [Google Scholar]
  6. E. R. Mardis, Next-generation DNA sequencing methods, Annual Review of Genomics and Human Genetics 9, pp. 387–402 (2008) [CrossRef] [PubMed] [Google Scholar]
  7. J. Eid, A. Fehr, J. Gray, K. Luong, J. Lyle, G. Otto, P. Peluso, D. Rank, P. Baybayan, B. Bettman, A. Bibillo, K. Bjornson, B. Chaudhuri, F. Christians, R. Cicero, S. Clark, R. Dalal, A. DeWinter, J. Dixon, M. Foquet, A. Gaertner, P. Hardenbol, C. Heiner, K. Hester, D. Holden, G. Kearns, X. Kong, R. Kuse, Y. Lacroix, S. Lin, P. Lundquist, C. Ma, P. Marks, M. Maxham, D. Murphy, I. Park, T. Pham, M. Phillips, J. Roy, R. Sebra, G. Shen, J. Sorenson, A. Tomaney, K. Travers, M. Trulson, J. Vieceli, J. Wegener, D. Wu, A. Yang, D. Zaccarin, P. Zhao, F. Zhong, J. Korlach, S. Turner, Real-time DNA sequencing from single polymerase molecules, Science 323, 5910, pp. 133–138 (2009) [CrossRef] [PubMed] [Google Scholar]
  8. W. J. Ansorge, Next-generation DNA sequencing techniques, New Biotechnology 25, 4, pp. 195–203 (2009) [CrossRef] [PubMed] [Google Scholar]
  9. J. W. Baurley, C. S. McMahan, C. M. Ervin, B. Pardamean, A. W. Bergen, Biosignature discovery for substance use disorders using statistical learning, Trends in Molecular Medicine 24, 2, pp. 221–235 (2019) [Google Scholar]
  10. C. Joyner, C. McMahan, J. Baurley, B. Pardamean, A two-phase bayesian methodology for the analysis of binary phenotypes in genome-wide association studies, Biometrical J. 62, 1, pp. 191–201 (2020) [CrossRef] [PubMed] [Google Scholar]
  11. D. Sudigyo, G. Rahmawati, D. W. Setiasari, R. H. Poluan, T. W. Cenggoro, A. Budiarto, A. A. Hidayat, S. R. Indrasari, Afiahayati, S. M. Haryana, B. Pardamean, Bioinformatics pathway analysis pipeline for NGS transcriptome profile data on nasopharyngeal carcinoma, IOP Conf. Series: Earth and Environmental Science 794, 1, pp. 1–10 (2021) [Google Scholar]
  12. I. Yusuf, B. Pardamean, J. W. Baurley, A. Budiarto, U. A. Miskad, R. E. Lusikooy, A. Arsyad, A. Irwan, G. Mathew, I. Suriapranata, R. Kusuma, M. F. Kacamarga, T. W. Cenggoro, C. McMahan, C. Joyner, C. I. Pardamean, Genetic risk factors for colorectal cancer in multiethnic Indonesians, Scientific Reports 11, 9988, pp. 1–9 (2021) [CrossRef] [PubMed] [Google Scholar]
  13. A. Budiarto, B. Mahesworo, A. A. Hidayat, I. Nurlaila, B. Pardamean, Gaussian mixture model implementation for population stratification estimation from genomics data, Procedia Computer Science 179, pp. 202–210 (2021) [CrossRef] [Google Scholar]
  14. D. E. Parung, K. Azizatikarna, D. Amirulloh, E. Listiyaningsih, B. Mahesworo, A. Budiarto, Simon, B. Pardamean, DNAku consumers profile: one of the first direct to customer genetics testing in Indonesia, IOP Conf. Series: Earth and Environmental Science 794, pp. 1–9 (2021) [Google Scholar]
  15. A. Budiarto, B. Pardamean, Explainable supervised method for genetics ancestry estimation, in 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) (2021) [Google Scholar]
  16. B. E. Slatko, A. F. Gardner, F. M. Ausubel, Overview of next generation sequencing technologies, Molecular Biology 122, 1, pp. 1–15 (2018) [Google Scholar]
  17. V. Tripathi, P. Kumar, P. Tripathi, A. Kishore, M. Kamle, Next-generation sequencing (NGS) platforms: an exciting era of genome sequence analysis, in Microbial Genomics in Sustainable Agroecosystems, pp. 89–110 (Springer, 2019) [Google Scholar]
  18. J. E. Barrick, D. S. Yu, S. H. Yoon, H. Jeong, T. K. Oh, D. Schneider, R. E. Lenski, J. F. Kim, Genome evolution and adaptation in a long-term experiment with escherichia coli, Nature 461, pp. 1243–1247 (2009) [CrossRef] [PubMed] [Google Scholar]
  19. Z. D. Blount, J. E. Barrick, C. J. Davidson, R. E. Lenski, Genomic analysis of a key innovation in an experimental escherichia coli population, Nature 489, pp. 513–518 (2012) [CrossRef] [PubMed] [Google Scholar]
  20. A. Budiarto, B. Mahesworo, J. Baurley, T. Suparyanto, B. Pardamean, Fast and effective clustering method for ancestry estimation, Procedia Computer Science 157, pp. 306–312 (2019) [CrossRef] [Google Scholar]
  21. B. Mahesworo, A. Budiarto, B. Pardamean, Systematic evaluation of cross population polygenic risk score on colorectal cancer, Procedia Computer Science, pp. 1–8 (2020) [Google Scholar]
  22. S. Amadeus, T. W. Cenggoro, A. Budiarto, B. Pardamean, A design of polygenic risk model with deep learning for colorectal cancer in multiethnic Indonesians, Procedia Computer Science 179, 2020, pp. 632-639 (2021) [CrossRef] [Google Scholar]
  23. K. Chen, J. W. Wallis, M. D. McLellan, D. E. Larson, J. M. Kalicki, C. S. Pohl, S. D. McGrath, M. C. Wendl, Q. Zhang, D. P. Locke, X. Shi, R. S. Fulton, T. J. Ley, R. K. Wilson, L. Ding, E. R. Mardis, BreakDancer: an algorithm for high-resolution mapping of genomic structural variation, Nature Methods 6, pp. 677–681 (2009) [CrossRef] [PubMed] [Google Scholar]
  24. B. Zeitouni, V. Boeva, I. Janoueix-Lerosey, S. Loeilleté, P. Legoix-n, A. Nicolas, O. Delattre, E. Barillot, SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data, Bioinformatics 26, 15, pp. 1895–1896 (2010) [CrossRef] [PubMed] [Google Scholar]
  25. S. Linnarsson, Recent advances in DNA sequencing methods - general principles of sample preparation, Experimental Cell Research 316, 8, pp. 1339–1343 (2010) [CrossRef] [PubMed] [Google Scholar]
  26. A. Healey, A. Furtado, T. Cooper, R. J. Henry, Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species, Plant Methods 10, 1, pp. 1–8 (2014) [Google Scholar]
  27. S. R. Head, H. Kiyomi Komori, S. A. LaMere, T. Whisenant, F. Van Nieuwerburgh, D. Salomon, P. Ordoukhanian, Library construction for next-generation sequencing: overviews and challenges, BioTechniques 56, 2, pp. 61–77 (2014) [CrossRef] [PubMed] [Google Scholar]
  28. R. M. Martin, M. Kausch, K. Yap, J. D. Wehr, G. L. Boyer, S. W. Wilhelm, Metagenome-assembled genome sequences of raphidiopsis raciborskii and planktothrix agardhii from a cyanobacterial bloom in kissena lake, New York, USA, Microbiology Resource Announcements 10, 2, pp. 10–11 (2021) [Google Scholar]
  29. J. S. Boden, M. Grego, H. Bolhuis, P. Sánchez-baracaldo, Draft genome sequences of three filamentous cyanobacteria isolated from brackish habitats, J. Genomics 9, pp. 20–25 (2021) [CrossRef] [Google Scholar]
  30. A. V. Bryanskaya, A. A. Shipova, A. S. Rozanov, O. A. Volkova, E. V. Lazareva, Y. E. Uvarova, T. N. Goryachkovskaya, S. E. Peltek, Metagenomics dataset used to characterize microbiome in water and sediments of the lake solenoe (novosibirsk region, Russia), Data in Brief 34, 106709 (2021) [CrossRef] [PubMed] [Google Scholar]
  31. J. E. Barrick, G. Colburn, D. E. Deatherage, C. C. Traverse, M. D. Strand, J. J. Borges, D. B. Knoester, A. Reba, A. G. Meyer, Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq, BMC Genomics 15, 1039, pp. 1–17 (2014) [CrossRef] [PubMed] [Google Scholar]
  32. D. E. Deatherage, J. E. Barrick, Identification of mutations in laboratory evolved microbes from next-generation sequencing data using breseq, Methods in Molecular Biology 1151, pp. 165–188 (2015) [CrossRef] [Google Scholar]
  33. S. Diamond, B. E. Rubin, R. K. Shultzaberger, Y. Chen, C. D. Barber, S. S. Golden, Redox crisis underlies conditional light-dark lethality in cyanobacterial mutants that lack the circadian regulator, RpaA, Proceedings of the National Academy of Sciences of the United States of America 114, 4, E580–9 (2017) [CrossRef] [PubMed] [Google Scholar]
  34. K. S. Walter, C. Colijn, T. Cohen, B. Mathema, Q. Liu, J. Bowers, D. M. Engelthaler, A. Narechania, D. Lemmer, J. Croda, J. R. Andrews, Genomic variant-identification methods may alter mycobacterium tuberculosis transmission inferences, Microbial Genomics 6, 8, pp. 1–16 (2020) [CrossRef] [Google Scholar]
  35. H. Derakhshani, S. P. Bernier, V. A. Marko, M. G. Surette, Completion of draft bacterial genomes by long-read sequencing of synthetic genomic pools, BMC Genomics 21, 519, pp. 1–11 (2020) [CrossRef] [Google Scholar]
  36. S. R. Miller, H. E. Abresch, N. J. Ulrich, E. B. Sano, A. H. Demaree, A. R. Oman, A. I. Garber, Bacterial adaptation by a transposition burst of an invading IS element, Genome Biology and Evolution 13, 11, pp. 1–12 (2021) [Google Scholar]
  37. B. Langmead, S. L. Salzberg, Fast gapped-read alignment with bowtie 2, Nature Methods 9, pp. 357–360 (2012) [CrossRef] [PubMed] [Google Scholar]
  38. P. Danecek, A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E. Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin, 1000 Genomes project analysis, 2011, the variant call format and VCFtools, Bioinformatics 27, 15, pp. 2156–2158 (2011) [CrossRef] [PubMed] [Google Scholar]
  39. M. G. Reese, B. Moore, C. Batchelor, F. Salas, F. Cunningham, G. T. Marth, L. Stein, P. Flicek, M. Yandell, K. Eilbeck, A standard variation file format for human genome sequences, Genome biology 11, R88, pp. 1–9 (2010) [CrossRef] [PubMed] [Google Scholar]
  40. H. Thorvaldsdóttir, J. T. Robinson, J. P. Mesirov, Integrative genomics viewer (!GV): high-performance genomics data visualization and exploration, Briefings in Bioinformatics 14, 2, pp. 178–192 (2013) [CrossRef] [PubMed] [Google Scholar]
  41. U. Väli, M. Brandström, M. Johansson, H. Ellegren, Insertion-deletion polymorphisms (indels) as genetic markers in natural populations, BMC genetics 9, pp. 1–8 (2008) [PubMed] [Google Scholar]
  42. R. Ohbayashi, S. Hirooka, R. Onuma, Y. Kanesaki, Y. Hirose, Y. Kobayashi, T. Fujiwara, C. Furusawa, S. Miyagishima, Evolutionary changes in dnaA-dependent chromosomal replication in cyanobacteria, Frontiers in Microbiology 11, 786, pp. 1–14 (2020) [CrossRef] [PubMed] [Google Scholar]
  43. M. Dann, E. M. Ortiz, M. Thomas, A. Guljamow, M. Lehmann, H. Schaefer, D. Leister, Enhancing photosynthesis at high light levels by adaptive laboratory evolution, Nature Plants 7, pp. 681–695 (2021) [CrossRef] [PubMed] [Google Scholar]
  44. W. Xu, H. Tang, Y. Wang, P. R. Chitnis, Proteins of the cyanobacterial photosystem I, Biochimica et Biophysica Acta 1507, 1–3, pp. 32–40 (2001) [CrossRef] [PubMed] [Google Scholar]
  45. R. M. Anur, N. Mufithah, W. D. Sawitri, H. Sakakibara, B. Sugiharto, Overexpression of sucrose phosphate synthase enhanced sucrose content and biomass production in transgenic sugarcane, Plants 9, 200, pp. 1–11 (2020) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.