ΕΥΒΙΔ

ΕΥΒΙΔ
Εργαστήριο Υπολογιστικής Βιολογίας και Διεργασιών

ΕΥΒΙΔ
Εργαστήριο Υπολογιστικής Βιολογίας και Διεργασιών

Mission

The Biological Computation & Process Laboratory (BCPL) is a research division of CPERI, established in 2014, under the ‘New Activities’ section of the Institute.

Goals:

  • High-performance computational analysis for biological systems engineering;
  • App development for energy, environment, security, biochemical processes & synthetic biology;
  • Support CPERI for all aspects of biotech data and resources.

Research focus

  • Advanced research in mainstream computational biology, algorithms & databases;
  • Collaborative research in translational bioinformatics, variomics & exposomics;
  • Exploratory research at the interface of chemical engineering & biomolecular research;
  • Innovative services for genomic technology, analysis & synthesis for biotechnology.

Main scientific directions

Genome technology

  • Genome analysis, annotation & interpretation of genomics data;
  • ChIP-seq, RNA-seq, expression analysis of complex genomic sequences;
  • Comparative transcriptomics & proteomics;
  • Metabolic pathway inference from genomic annotation, large-scale methods.

Genome science

  • Whole-genome comparison, metrics for species/strain taxonomy & classification;
  • Metabolic reconstruction, pathway detection & evolutionary history inference;
  • Horizontal gene transfer, detection & quantification, the Last Universal Common Ancestor.

Algorithms & databases

  • Genome databases, pangenomes, ortholog detection;
  • Genome-aware algorithms for phylogenetic profiles, gene clusters, gene fusions;
  • Bias detection & intrinsic protein disorder;
  • Sequence matching, network visualization, large-scale sequence comparison.

Systems biology/biotechnology

  • Functional module detection;
  • Protein interaction detection, multi-domain decomposition of protein families;
  • Translational bioinformatics, pathological versus physiological stress;
  • Systems radiobiology, low-dose ionizing radiation exposure;
  • Exposomics.

Science policy

  • Science communication: history of science, metaphors in science, ancient sciencep
  • Community actions for research & training: GEBA, GSC, Mikrobiokosmos, HSCBB, HBio.info, ISCB;
  • Historical development of a new field: computation in biology, and beyond.

Synthetic biology/biotechnology

  • Functional module detection
  • Ancestral state reconstruction;
  • Metabolic innovation, synthetic design of reaction & pathway variants;
  • Astrobiology: life detection protocols, universal reactions.

Έργα & Δράσεις

ΕΥΒΙΔ

Elixir Hub

/GR-CERTH-2021-IDP 

Από: 07/09/2021
Έως: 07/08/2023
25 μήνες
ΕΥΒΙΔ

Elixir-Converge

Από: 02/01/2020
Έως: 08/10/2024
36 μήνες
ΕΥΒΙΔ

Elixir-GR

/MIS 5002780 

Από: 07/01/2018
Έως: 08/10/2024
39 μήνες

Προσωπικό

ΕΥΒΙΔ

Ουζούνης Χρήστος

Συνεργαζόμενο Μέλος ΔΕΠ
, Καθηγητής
, ΑΠΘ, Τμήμα Βιολογίας
ΕΥΒΙΔ

Χασάπη Αναστασία

Συνεργαζόμενος Ερευνητής, PhD
, PhD, Βιοπληροφορικός

Δημοσιεύσεις

Quaglia F., Mészáros B., Salladini E., Hatos A., Pancsa R., Chemes L., Pajkos M., Lazar T., Peña-Díaz S., Santos J., Ács V., Farahi N., Fichó E., Aspromonte M., Bassot C., Chasapi A., Davey N., Davidović R., Dobson L., Elofsson A., Erdős G., Gaudet P., Giglio M., Glavina J., Iserte J., Iglesias V., Kálmán Z., Lambrughi M., Leonardi E., Longhi S., Macedo-Ribeiro S., Maiani E., Marchetti J., Marino-Buslje C., Mészáros A., Monzon A., Minervini G., Nadendla S., Nilsson J., Novotný M., Ouzounis C., Palopoli N., Papaleo E., Barbosa Pereira P., Pozzati G, Promponas V., Pujols J., Rocha S., Salas M., Sawicki R., Schad E., Shenoy A., Szaniszló T., Tsirigos K., Veljkovic N., Parisi G., Ventura S., Dosztányi Z., Tompa P., Tosatto S., Piovesan D.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

2022

Nucleic Acids Research 50 (D1), D480-D487

Mueller Y., Schrama T., Ruijten R., Schreurs M., Grashof D., van de Werken H., Lasinio G., Álvarez-Sierra D., Kiernan C., Castro Eiro M., van Meurs M., Brouwers-Haspels I., Zhao M., Li L., de Wit H., Ouzounis C., Wilmsen M., Alofs T., Laport D., van Wees T., Kraker G., Jaimes M., van Bockstael S., Hernández-González M., Rokx C., Rijnders B., Pujol-Borrell R., Katsikis P.

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning

2022

Nature Communications 13 (915)

Danko D., et. al

A global metagenomic map of urban microbiomes and antimicrobial resistance

2021

Cell 184 (13), 3376-3393.e17

Karapiperis C., Kouklis P., Papastratos S., Chasapi A., Danchin A., Angelis L., Ouzounis C.

A strong seasonality pattern for COVID-19 incidence rates modulated by UV radiation levels

2021

Viruses 13 (4), 574

Neches R., Kyrpides N., Ouzounis C.

Atypical Divergence of SARS-CoV-2 Orf8 from Orf7a within the Coronavirus Lineage Suggests Potential Stealthy Viral Strategies in Immune Evasion

2021

mBio 12 (1), e03014-2

M Necci M., D Piovesan D., CAID Predictors, DISPROT Curators, Tosatto S.

Critical assessment of protein intrinsic disorder prediction

2021

Nature Methods 18, 472–481

Mueller Y., Schrama T., Ruijten R., Schreurs M., Grashof D., van de Werken H., Alvarez de la Sierra D., Kiernan C., Castro Eiro M., van Meurs M., Brouwers-Haspels I., Zhao M., Li L., de Wit H., Ouzounis C., Wilmsen M., Alofs T., Laport D., van Wees T., Kraker G., Jaimes M., van Bockstael S., Hernández-González M., Rokx C., Rijnders B., Pujol-Borrell R., Katsikis P.

Immunophenotyping and machine learning identify distinct immunotypes that predict COVID-19 clinical severity

2021

medRxiv

Mier P., Paladin L., Tamana S., Petrosian S., Hajdu-Soltész B., Urbanek A., Gruca A., Plewczynski D., Grynberg M., Bernadó P., Gáspári Z., Ouzounis C., Promponas V., Kajava a., Hancock j., Tosatto S., Dosztanyi Z., Andrade-Navarro Μ.

Disentangling the complexity of low complexity proteins

2020

Briefings in Bioinformatics 21 (2), 458-472

Quaglia F., Mészáros B., Salladini E., Hatos A., Pancsa R., Chemes L., Pajkos M., Lazar T., Peña-Díaz S., Santos J., Ács V., Farahi N., Fichó E., Aspromonte M., Bassot C., Chasapi A., Davey N., Davidović R., Dobson L., Elofsson A., Erdős G., Gaudet P., Giglio M., Glavina J., Iserte J., Iglesias V., Kálmán Z., Lambrughi M., Leonardi E., Longhi S., Macedo-Ribeiro S., Maiani E., Marchetti J., Marino-Buslje C., Mészáros A., Monzon A., Minervini G., Nadendla S., Nilsson J., Novotný M., Ouzounis C., Palopoli N., Papaleo E., Barbosa Pereira P., Pozzati G, Promponas V., Pujols J., Rocha S., Salas M., Sawicki R., Schad E., Shenoy A., Szaniszló T., Tsirigos K., Veljkovic N., Parisi G., Ventura S., Dosztányi Z., Tompa P., Tosatto S., Piovesan D.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

2022

Nucleic Acids Research 50 (D1), D480-D487

Mueller Y., Schrama T., Ruijten R., Schreurs M., Grashof D., van de Werken H., Lasinio G., Álvarez-Sierra D., Kiernan C., Castro Eiro M., van Meurs M., Brouwers-Haspels I., Zhao M., Li L., de Wit H., Ouzounis C., Wilmsen M., Alofs T., Laport D., van Wees T., Kraker G., Jaimes M., van Bockstael S., Hernández-González M., Rokx C., Rijnders B., Pujol-Borrell R., Katsikis P.

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning

2022

Nature Communications 13 (915)

Danko D., et. al

A global metagenomic map of urban microbiomes and antimicrobial resistance

2021

Cell 184 (13), 3376-3393.e17

Karapiperis C., Kouklis P., Papastratos S., Chasapi A., Danchin A., Angelis L., Ouzounis C.

A strong seasonality pattern for COVID-19 incidence rates modulated by UV radiation levels

2021

Viruses 13 (4), 574

Neches R., Kyrpides N., Ouzounis C.

Atypical Divergence of SARS-CoV-2 Orf8 from Orf7a within the Coronavirus Lineage Suggests Potential Stealthy Viral Strategies in Immune Evasion

2021

mBio 12 (1), e03014-2

M Necci M., D Piovesan D., CAID Predictors, DISPROT Curators, Tosatto S.

Critical assessment of protein intrinsic disorder prediction

2021

Nature Methods 18, 472–481

Mueller Y., Schrama T., Ruijten R., Schreurs M., Grashof D., van de Werken H., Alvarez de la Sierra D., Kiernan C., Castro Eiro M., van Meurs M., Brouwers-Haspels I., Zhao M., Li L., de Wit H., Ouzounis C., Wilmsen M., Alofs T., Laport D., van Wees T., Kraker G., Jaimes M., van Bockstael S., Hernández-González M., Rokx C., Rijnders B., Pujol-Borrell R., Katsikis P.

Immunophenotyping and machine learning identify distinct immunotypes that predict COVID-19 clinical severity

2021

medRxiv

Mier P., Paladin L., Tamana S., Petrosian S., Hajdu-Soltész B., Urbanek A., Gruca A., Plewczynski D., Grynberg M., Bernadó P., Gáspári Z., Ouzounis C., Promponas V., Kajava a., Hancock j., Tosatto S., Dosztanyi Z., Andrade-Navarro Μ.

Disentangling the complexity of low complexity proteins

2020

Briefings in Bioinformatics 21 (2), 458-472

Hatos A., Hajdu-Soltész B., Monzon M., Nicolas Palopoli N., Álvarez L., Aykac-Fas B., Bassot C., Benítez G., Bevilacqua M., Chasapi A., Chemes L., Davey N., Davidović R., Dunker K., Elofsson A., Gobeill J., Foutel G., Sudha G., Guharoy M., Horvath T., Iglesias V., Kajava A., Kovacs O., Lamb J., Lambrughi M., Lazar T., Leclercq J., Leonardi E., Macedo-Ribeiro S., Macossay-Castillo M., Maiani E., Manso J., Marino-Buslje C., Martínez-Pérez E., Mészáros B., Mičetić I., Minervini G., Murvai N., Necci M., Ouzounis C., Pajkos M., Paladin L., Pancsa R., Papaleo E., Parisi G., Pasche E., Barbosa Pereira P., Promponas V., Pujols J., Quaglia F., Ruch P., Salvatore M., Schad E., Szabo B., Szaniszló T., Tamana S., Tantos A., Veljkovic N., Ventura S., Vranken W., Dosztányi Z, Tompa P., Tosatto S., Piovesan D.

DisProt: intrinsic protein disorder annotation in 2020

2020

Nucleic acids research 48 (D1), D269-D276

Ouzounis C.

From natural dexterity to artificial inaptitude: MacOS 2010 to 2020 (opinion)

2020