Anastasios Oulas

@article{holm_first_2025,

title = {First island-wide, single-day soil collection study on Crete reveals environmental drivers of microbial diversity},

author = {Johanna B Holm and Savvas Paragkamian and Mike Humphreys and Apaala Chatterjee and Stephanie Yarwood and Josh Gaimaro and Melanthia Stavroulaki and Dimitris Tsaparis and Manolis Plaitis and Panagiotis Kasapidis and Stelios Darivianakis and Georgios Kotoulas and Antonios Magoulas and Anastasis Oulas and Zacharias Kyrpiotakis and Pavlos Pavlidis and Petra Ten Hoopen and Guy Cochrane and Wiebe H C F Kooistra and Jason R Schriml and Bronwen E Schriml and Ilias Lagkouvardos and Panos Gkorezis and Neil Davies and Christine Laney and Lee Stanish and Granger Sutton and Scott Tighe and Ilene Karsch Mizrachi and Donovan Parks and Pelin Yilmaz and Chelsea Carey and Pier Buttigieg and Philip Goldstein and Evangelos Pafilis and Lynn M Schriml},

url = {https://environmentalmicrobiome.biomedcentral.com/articles/10.1186/s40793-025-00752-z

/wp-content/uploads/2025/09/2025-Holm-Paragkamian-EnvMicro-46.pdf},

doi = {10.1186/s40793-025-00752-z},

issn = {2524-6372},

year  = {2025},

date = {2025-09-04},

urldate = {2025-09-08},

journal = {Environmental Microbiome},

volume = {20},

number = {1},

pages = {94},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{baltoumas_exploring_2023,

title = {Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters},

author = {Fotis A Baltoumas and Evangelos Karatzas and David Paez-Espino and Nefeli K Venetsianou and Eleni Aplakidou and Anastasis Oulas and Robert D Finn and Sergey Ovchinnikov and Evangelos Pafilis and Nikos C Kyrpides and Georgios A Pavlopoulos},

url = {https://imbbc.hcmr.gr/wp-content/uploads/2023/03/2023-Baltoumas-FroΒionform-14.pdf

https://www.frontiersin.org/articles/10.3389/fbinf.2023.1157956/full},

doi = {10.3389/fbinf.2023.1157956},

issn = {2673-7647},

year  = {2023},

date = {2023-03-08},

urldate = {2023-03-08},

journal = {Frontiers in Bioinformatics},

volume = {3},

pages = {1157956},

abstract = {Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{polymenakou_high_2020,

title = {High genetic diversity and variability of microbial communities in near-surface atmosphere of Crete island, Greece},

author = {Paraskevi N Polymenakou and Manolis Mandalakis and Michalis Macheras and Anastasis Oulas and Jon Bent Kristoffersen and Christos A Christakis and Vasso Terzoglou and Melanthia Stavroulaki},

url = {http://link.springer.com/10.1007/s10453-020-09636-w},

doi = {10.1007/s10453-020-09636-w},

issn = {0393-5965, 1573-3025},

year  = {2020},

date = {2020-09-01},

urldate = {2020-08-31},

journal = {Aerobiologia},

volume = {36},

number = {3},

pages = {341--353},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pavloudi_sediment_2017,

title = {Sediment microbial taxonomic and functional diversity in a natural salinity gradient challenge Remane's 'species minimum' concept},

author = {C Pavloudi and J B Kristoffersen and A Oulas and M De Troch and C Arvanitidis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031101355&doi=10.7717%2fpeerj.3687&partnerID=40&md5=6860a76d415733f23eeb5d4df199a4d5},

doi = {10.7717/peerj.3687},

issn = {21678359},

year  = {2017},

date = {2017-01-01},

journal = {PeerJ},

volume = {2017},

number = {10},

abstract = {Several models have been developed for the description of diversity in estuaries and other brackish habitats, with the most recognized being Remane's Artenminimum ("species minimum") concept. It was developed for the Baltic Sea, one of the world's largest semi-enclosed brackish water body with a unique permanent salinity gradient, and it argues that taxonomic diversity of macrobenthic organisms is lowest within the horohalinicum (5 to 8 psu). The aim of the present study was to investigate the relationship between salinity and sediment microbial diversity at a freshwater-marine transect in Amvrakikos Gulf (Ionian Sea, Western Greece) and assess whether species composition and community function follow a generalized concept such as Remane's. DNA was extracted from sediment samples from six stations along the aforementioned transect and sequenced for the 16S rRNA gene using high-throughput sequencing. The metabolic functions of the OTUs were predicted and the most abundant metabolic pathways were extracted. Key abiotic variables, i.e., salinity, temperature, chlorophyll-a and oxygen concentration etc., were measured and their relation with diversity and functional patterns was explored. Microbial communities were found to differ in the three habitats examined (river, lagoon and sea) with certain taxonomic groups being more abundant in the freshwater and less in the marine environment, and vice versa. Salinity was the environmental factor with the highest correlation to the microbial community pattern, while oxygen concentration was highly correlated to the metabolic functional pattern. The total number of OTUs showed a negative relationship with increasing salinity, thus the sediment microbial OTUs in this study area do not follow Remane's concept. © 2017 Pavloudi et al.},

note = {Publisher: PeerJ Inc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pavloudi_diversity_2017,

title = {Diversity and abundance of sulfate-reducing microorganisms in a Mediterranean lagoonal complex (Amvrakikos Gulf, Ionian Sea) derived from dsrB gene},

author = {C Pavloudi and A Oulas and K Vasileiadou and G Kotoulas and M De Troch and M W Friedrich and C Arvanitidis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021092263&doi=10.3354%2fame01829&partnerID=40&md5=8be345d2d8f932cc9b640cf2072c2bb0},

doi = {10.3354/ame01829},

issn = {09483055},

year  = {2017},

date = {2017-01-01},

journal = {Aquatic Microbial Ecology},

volume = {79},

number = {3},

pages = {209--219},

abstract = {Sulfate-reducing microorganisms (SRMs) are a phylogenetically and physiologically diverse group of microorganisms, responsible for the dissimilatory reduction of sulfate. SRMs thrive under anaerobic conditions with high availability of organic matter. Such conditions characterize lagoonal ecosystems which experience regular dystrophic crises. The aim of the present study was to explore the biodiversity patterns of SRMs and to examine the extent to which these patterns are associated with biogeographic and environmental factors. Sediment samples were collected from 5 lagoons in the Amvrakikos Gulf (Ionian Sea, western Greece). DNA was extracted from the sediment and was further processed through pyrosequencing of a region of the dissimilatory sulfite reductase β-subunit (dsrB). The results of this exploratory study show that the majority of the observed operational taxonomic units (OTUs) belong to the Deltaproteobacteria supercluster and more specifically, to the Desulfobacteraceae family. Salinity and ammonium ions are the environmental factors that best correlated with the SRM community pattern. Furthermore, the SRM community of the brackish lagoons is differentiated from that of the brackish-marine lagoons and the studied lagoons have distinct SRM communities. © Inter-Research 2017.},

note = {Publisher: Inter-Research},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{faulwetter_emodnet_2016,

title = {EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases},

author = {Sarah Faulwetter and Evangelos Pafilis and Lucia Fanini and Nicolas Bailly and Donat Agosti and Christos Arvanitidis and Laura Boicenco and Terry Capatano and Simon Claus and Stefanie Dekeyzer and Teodor Georgiev and Aglaia Legaki and Dimitra Mavraki and Anastasis Oulas and Gabriella Papastefanou and Lyubomir Penev and Guido Sautter and Dmitry Schigel and Viktor Senderov and Adrian Teaca and Marilena Tsompanou},

url = {http://rio.pensoft.net/articles.php?id=9774},

doi = {10.3897/rio.2.e9774},

issn = {2367-7163},

year  = {2016},

date = {2016-07-01},

urldate = {2020-09-21},

journal = {Research Ideas and Outcomes},

volume = {2},

pages = {e9774},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pavloudi_salinity_2016,

title = {Salinity is the major factor influencing the sediment bacterial communities in a Mediterranean lagoonal complex (Amvrakikos Gulf, Ionian Sea)},

author = {C Pavloudi and A Oulas and K Vasileiadou and E Sarropoulou and G Kotoulas and C Arvanitidis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955611416&doi=10.1016%2fj.margen.2016.01.005&partnerID=40&md5=a226c6872c0a3044831db4a1b5f65ca3},

doi = {10.1016/j.margen.2016.01.005},

issn = {18747787},

year  = {2016},

date = {2016-01-01},

journal = {Marine Genomics},

volume = {28},

pages = {71--81},

abstract = {Lagoons are naturally enriched habitats, with unstable environmental conditions caused by their confinement, shallow depth and state of saprobity. The frequent fluctuations of the abiotic variables cause severe changes in the abundance and distribution of biota. This relationship has been studied extensively for the macrofaunal communities, but not sufficiently so for the bacterial ones. The aim of the present study was to explore the biodiversity patterns of bacterial assemblages and to examine whether these patterns are associated with biogeographic and environmental factors. For this purpose, sediment samples were collected from five lagoons located in the Amvrakikos Gulf (Ionian Sea, Western Greece). DNA was extracted from the sediment and was further processed through 16S rRNA pyrosequencing. The results of this exploratory study imply that salinity is the environmental factor best correlated with the bacterial community pattern, which has also been suggested in similar studies but for macrofaunal community patterns. In addition, the bacterial community of the brackish lagoons is differentiated from that of the brackish-marine lagoons. The findings of this study indicate that the studied lagoons have distinct bacterial communities. © 2016 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{varsos_optimized_2016,

title = {Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab)},

author = {C Varsos and T Patkos and A Oulas and C Pavloudi and A Gougousis and U Z Ijaz and I Filiopoulou and N Pattakos and E V Berghe and A Fernández-Guerra and S Faulwetter and E Chatzinikolaou and E Pafilis and C Bekiari and M Doerr and C Arvanitidis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018640003&doi=10.3897%2fBDJ.4.e8357&partnerID=40&md5=6003e9caf6582fdde7f4bffec91e154f},

doi = {10.3897/BDJ.4.e8357},

issn = {13142828},

year  = {2016},

date = {2016-01-01},

journal = {Biodiversity Data Journal},

volume = {4},

number = {1},

abstract = {Background Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/ microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. New information In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data - Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pclinux- gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https:// portal.lifewatchgreece.eu/.},

note = {Publisher: Pensoft Publishers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bobrova_metagenomic_2016,

title = {Metagenomic 16s rRNA investigation of microbial communities in the Black Sea estuaries in South-West of Ukraine},

author = {O Bobrova and J B Kristoffersen and A Oulas and V Ivanytsia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976871399&doi=10.18388%2Fabp.2015_1145&partnerID=40&md5=ede0e6981e07f9f419640e4b2c605d1e},

doi = {10.18388/abp.2015_1145},

issn = {0001527X},

year  = {2016},

date = {2016-01-01},

journal = {Acta Biochimica Polonica},

volume = {63},

number = {2},

pages = {315--319},

abstract = {The Black Sea estuaries represent interfaces of the sea and river environments. Microorganisms that inhabit estuarine water play an integral role in all biochemical processes that occur there and form unique ecosystems. There are many estuaries located in the Southern-Western part of Ukraine and some of them are already separated from the sea. The aim of this research was to determine the composition of microbial communities in the Khadzhibey, Dniester and Sukhyi estuaries by metagenomic 16S rDNA analysis. This study is the first complex analysis of estuarine microbiota based on isolation of total DNA from a biome that was further subjected to sequencing. DNA was extracted from water samples and sequenced on the Illumina Miseq platform using primers to the V4 variable region of the 16S rRNA gene. Computer analysis of the obtained raw sequences was done with QIIME (Quantitative Insights Into Microbial Ecology) software. As the outcome, 57970 nucleotide sequences were retrieved. Bioinformatic analysis of bacterial community in the studied samples demonstrated a high taxonomic diversity of Prokaryotes at above genus level. It was shown that majority of 16S rDNA bacterial sequences detected in the estuarine samples belonged to phyla Cyanobacteria, Proteobacteria, Bacteroidetes, Actinobacteria, Verrucomicrobia, Planctomycetes. The Khadhzibey estuary was dominated by the Proteobacteria phylum, while Dniester and Sukhyi estuaries were characterized by dominance of Cyanobacteria. The differences in bacterial populations between the Khadzhibey, Dniester and Sukhyi estuaries were demonstrated through the Beta-diversity analysis. It showed that the Khadzhibey estuary's microbial community significantly varies from the Sukhyi and Dniester estuaries. The majority of identified bacterial species is known as typical inhabitants of marine environments, however, for 2.5% of microbial population members in the studied estuaries no relatives were determined.},

note = {Publisher: Polskie Towarzystwo Biochemiczne},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sinclair_seqenv_2016,

title = {Seqenv: Linking sequences to environments through text mining},

author = {L Sinclair and U Z Ijaz and L J Jensen and M J L Coolen and C Gubry-Rangin and A Chroňáková and A Oulas and C Pavloudi and J Schnetzer and A Weimann and A Ijaz and A Eiler and C Quince and E Pafilis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007364069&doi=10.7717%2fpeerj.2690&partnerID=40&md5=d6fb9302c27b05c3c656dcf2f8aa9512},

doi = {10.7717/peerj.2690},

issn = {21678359},

year  = {2016},

date = {2016-01-01},

journal = {PeerJ},

volume = {2016},

number = {12},

abstract = {Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts-if it is available-the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv. © 2016 Sinclair et al.},

note = {Publisher: PeerJ Inc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_metagenomic_2016,

title = {Metagenomic investigation of the geologically unique Hellenic Volcanic Arc reveals a distinctive ecosystem with unexpected physiology.},

author = {Anastasis Oulas and Paraskevi N Polymenakou and Rekha Seshadri and James H Tripp and Manolis Mandalakis and David A Paez-Espino and Amrita Pati and Patrick Chain and Paraskevi Nomikou and Steven Carey and Stephanos Kilias and Christos Christakis and Georgios Kotoulas and Antonios Magoulas and Natalia N Ivanova and Nikos C Kyrpides},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26487573},

doi = {10.1111/1462-2920.13095},

issn = {1462-2920},

year  = {2016},

date = {2016-01-01},

journal = {Environmental Microbiology},

volume = {18},

number = {4},

pages = {1122--1136},

abstract = {Hydrothermal vents represent a deep, hot, aphotic biosphere where chemosynthetic primary producers, fuelled by chemicals from Earth's subsurface, form the basis of life. In this study, we examined microbial mats from two distinct volcanic sites within the Hellenic Volcanic Arc (HVA). The HVA is geologically and ecologically unique, with reported emissions of CO2 -saturated fluids at temperatures up to 220°C and a notable absence of macrofauna. Metagenomic data reveals highly complex prokaryotic communities composed of chemolithoautotrophs, some methanotrophs, and to our surprise, heterotrophs capable of anaerobic degradation of aromatic hydrocarbons. Our data suggest that aromatic hydrocarbons may indeed be a significant source of carbon in these sites, and instigate additional research into the nature and origin of these compounds in the HVA. Novel physiology was assigned to several uncultured prokaryotic lineages; most notably, a SAR406 representative is attributed with a role in anaerobic hydrocarbon degradation. This dataset, the largest to date from submarine volcanic ecosystems, constitutes a significant resource of novel genes and pathways with potential biotechnological applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{polymenakou_pyrosequencing_2015,

title = {Pyrosequencing analysis of microbial communities reveals dominant cosmopolitan phylotypes in deep-sea sediments of the eastern Mediterranean Sea},

author = {Paraskevi N Polymenakou and Christos A Christakis and Manolis Mandalakis and Anastasis Oulas},

url = {http://www.sciencedirect.com/science/article/pii/S0923250815000571},

doi = {http://dx.doi.org/10.1016/j.resmic.2015.03.005},

issn = {0923-2508},

year  = {2015},

date = {2015-06-01},

journal = {Research in Microbiology},

volume = {166},

number = {5},

pages = {448--457},

abstract = {Abstract The deep eastern basin of the Mediterranean Sea is considered to be one of the world's most oligotrophic areas in the world. Here we performed pyrosequenicng analysis of bacterial and archaeal communities in oxic nutrient-poor sediments collected from the eastern Mediterranean at 1025–4393 m depth. Microbial communities were surveyed by targeting the hypervariable V5–V6 regions of the 16S ribosomal RNA gene using bar-coded pyrosequencing. With a total of 13,194 operational taxonomic units (OTUs) or phylotypes at 97% sequence similarities, the phylogenetic affiliation of microbes was assigned to 23 bacterial and 2 archaeal known phyla, 23 candidate divisions at the phylum level and distributed into 186 families. It was further revealed that the microbial consortia inhabiting all sampling sites were highly diverse, but dominated by phylotypes closely related to members of the genus Pseudomonas and Marine Group I archaea. Such pronounced and widespread enrichment probably manifests the cosmopolitan character of these species and raises questions about their metabolic adaptation to the physical stressors and low nutrient availability of the deep eastern Mediterranean Sea.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_metagenomics_2015,

title = {Metagenomics: Tools and insights for analyzing next-generation sequencing data derived from biodiversity studies},

author = {A Oulas and C Pavloudi and P Polymenakou and G A Pavlopoulos and N Papanikolaou and G Kotoulas and C Arvanitidis and I Iliopoulos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981483022&doi=10.4137%2fBBI.S12462&partnerID=40&md5=c1dbc4e466d691d4a86eecd6ba7d7a08},

doi = {10.4137/BBI.S12462},

issn = {11779322},

year  = {2015},

date = {2015-01-01},

journal = {Bioinformatics and Biology Insights},

volume = {9},

pages = {75--88},

abstract = {Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of "metagenomics", often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards. © the authors.},

note = {Publisher: Libertas Academica Ltd.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{velegraki_impaired_2013,

title = {Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production},

author = {M Velegraki and E Papakonstanti and I Mavroudi and M Psyllaki and C Tsatsanis and A Oulas and I Iliopoulos and P Katonis and H A Papadaki},

url = {http://www.haematologica.org/cgi/doi/10.3324/haematol.2012.064642},

doi = {10.3324/haematol.2012.064642},

issn = {0390-6078, 1592-8721},

year  = {2013},

date = {2013-08-01},

urldate = {2020-08-18},

journal = {Haematologica},

volume = {98},

number = {8},

pages = {1206--1215},

abstract = {Excessive pro-inflammatory cytokine production in the bone marrow has been associated with the pathogenesis of myelodysplastic syndromes. We herein investigated the involvement of toll-like receptors and their endogenous ligands in the induction/maintenance of the inflammatory process in the marrow of patients with myelodysplastic syndromes. We evaluated the expression of toll-like receptors in marrow monocytes of patients (n=27) and healthy controls (n=25) by flow-cytometry and also assessed the activation of the respective signaling using a realtime polymerase chain reaction-based array. We measured the high mobility group box-1 protein, a toll-like receptor- 4 ligand, in marrow plasma and long-term bone marrow culture supernatants by an enzyme-linked immunosorbent assay and we performed cross-over experiments using marrow plasma from patients and controls in the presence/absence of a toll-like receptor-4 inhibitor to evaluate the pro-inflammatory cytokine production by chemiluminescence. We assessed the apoptotic cell clearance capacity of patients’ macrophages using a fluorescence microscopy-based assay. We found over-expression of toll-like receptor-4 in patients’ marrow monocytes compared to that in controls; this over-expression was associated with up-modulation of 53 genes related to the respective signaling. Incubation of patients’ monocytes with autologous, but not with normal, marrow plasma resulted in over-production of pro-inflammatory cytokines, an effect that was abrogated by the toll-like receptor- 4 inhibitor suggesting that the pro-inflammatory cytokine production in myelodysplastic syndromes is largely mediated through toll-like receptor-4. The levels of high mobility group box-1 protein were increased in patients’ marrow plasma and culture supernatants compared to the levels in controls. Patients’ macrophages displayed an impaired capacity to engulf apoptotic cells and this defect was associated with excessive release of high mobility group box-1 protein by dying cells. A primary apoptotic cell clearance defect of marrow macrophages in myelodysplastic syndromes may contribute to the induction/maintenance of the inflammatory process through aberrant release of molecules inducing toll-like receptor-4 such as high mobility group box-1 protein. © 2013 Ferrata Storti Foundation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pavlopoulos_unraveling_2013,

title = {Unraveling genomic variation from next generation sequencing data},

author = {G A Pavlopoulos and Anastasis Oulas and E Iacucci and A Sifrim and Y Moreau and R Schneider and J Aerts and I Iliopoulos},

url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880858660&partnerID=40&md5=d969a0439e9107b5c2c464eb29cf6b8d},

year  = {2013},

date = {2013-01-01},

journal = {BioData Mining},

volume = {6},

abstract = {Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field. © 2013 Pavlopoulos et al.; licensee BioMed Central Ltd.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_new_2012,

title = {A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2},

author = {Anastasis Oulas and N Karathanasis and A Louloupi and I Iliopoulos and K Kalantidis and P Poirazi},

url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84868137211&partnerID=40&md5=bf41cadd368ed0a4b3690e03e6622cd4},

doi = {https://doi.org/10.4161/rna.21725},

year  = {2012},

date = {2012-01-01},

journal = {RNA Biology},

volume = {9},

pages = {1196 -- 1207},

abstract = {Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about the strengths or weaknesses of each particular algorithm, the fact of the matter is that they fall substantially short of capturing the full detail of physical, temporal and spatial requirements of miRNA:target-mRNA interactions. Here, we introduce a novel miRNA target prediction tool called Targetprofiler that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets. Using a large scale protein downregulation data set we validate our method and compare its performance to existing tools. We find that Targetprofiler exhibits greater correlation between computational predictions and protein downregulation and predicts experimentally verified miRNA targets more accurately than three other tools. Concurrently, we use primer extension to identify the mature sequence of a novel miRNA gene recently identified within a cancer associated genomic region and use Targetprofiler to predict its potential targets. Experimental verification of the ability of this small RNA molecule to regulate the expression of CC ND2, a gene with documented oncogenic activity, confirms its functional role as a miRNA. These findings highlight the competitive advantage of our tool and its efficacy in extracting biologically significant results. © 2012 Landes Bioscience.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_finding_2011,

title = {Finding Cancer-Associated miRNAs: Methods and Tools},

author = {Anastasis Oulas and Nestoras Karathanasis and Annita Louloupi and Panayiota Poirazi},

url = {http://link.springer.com/10.1007/s12033-011-9416-4},

doi = {10.1007/s12033-011-9416-4},

issn = {1073-6085, 1559-0305},

year  = {2011},

date = {2011-09-01},

urldate = {2020-08-18},

journal = {Molecular Biotechnology},

volume = {49},

number = {1},

pages = {97--107},

abstract = {Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer. © 2011 Springer Science+Business Media, LLC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_prediction_2009,

title = {Prediction of novel microRNA genes in cancer-associated genomic regions—a combined computational and experimental approach},

author = {Anastasis Oulas and Alexandra Boutla and Katerina Gkirtzou and Martin Reczko and Kriton Kalantidis and Panayiota Poirazi},

url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkp120},

doi = {10.1093/nar/gkp120},

issn = {1362-4962, 0305-1048},

year  = {2009},

date = {2009-06-01},

urldate = {2020-08-18},

journal = {Nucleic Acids Research},

volume = {37},

number = {10},

pages = {3276--3287},

abstract = {The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oulas_micrornas_2009,

title = {MicroRNAs and cancer - The search begins!},

author = {A Oulas and M Reczko and P Poirazi},

url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-63449097593&partnerID=40&md5=b6372d1b089560f3cd5d4d64950614d2},

doi = {10.1109/TITB.2008.2007086},

issn = {1089-7771},

year  = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Information Technology in Biomedicine},

volume = {13},

pages = {67--77},

abstract = {For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer. © 2009 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{petalidis_improved_2008,

title = {Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data},

author = {L P Petalidis and A Oulas and M Backlund and M T Wayland and L Liu and K Plant and L Happerfield and T C Freeman and P Poirazi and V P Collins},

url = {http://mct.aacrjournals.org/cgi/doi/10.1158/1535-7163.MCT-07-0177},

doi = {10.1158/1535-7163.MCT-07-0177},

issn = {1535-7163, 1538-8514},

year  = {2008},

date = {2008-05-01},

urldate = {2020-08-18},

journal = {Molecular Cancer Therapeutics},

volume = {7},

number = {5},

pages = {1013--1024},

abstract = {Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified. Copyright © 2008 American Association for Cancer Research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}