BCPL

BCPL –
Biological Computation & Process Laboratory

BCPL – Biological Computation & Process Laboratory

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.

Projects & Activities

BCPL

Elixir Hub

/GR-CERTH-2021-IDP

BCPL

Elixir-Converge

BCPL

Elixir-GR

/MIS 5002780

Personnel

BCPL

Chasapi Anastasia

Collaborating Researcher PhD

, Computational Biologist, PhD

BCPL

Ouzounis Christos

Collaborating Faculty Member

, Professor

, AUTH, Biology Dept.

Publications

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

Kyritsis K., Ouzounis C., Angelis L., Vizirianakis I.

Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

2020

NAR genomics and bioinformatics 2 (4), lqaa088

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

Kyritsis K., Ouzounis C., Angelis L., Vizirianakis I.

Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

2020

NAR genomics and bioinformatics 2 (4), lqaa088

Chasapi A., Promponas V., Ouzounis C.

The bioinformatics wealth of nations

2020

Bioinformatics 36 (9), 2963-2965

Karapiperis C., Chasapi A., Angelis L., Scouras Z., Mastroberardino P., Tapio S., Atkinson M., Ouzounis C.

The coming of age for Big Data in Systems Radiobiology, an engineering perspective

2020

Big Data

BCPL

BCPL – Biological Computation & Process Laboratory

BCPL – Biological Computation & Process Laboratory

Mission

Research focus

Main scientific directions

Projects & Activities

Elixir Hub

Elixir-Converge

Elixir-GR

Personnel

Chasapi Anastasia

Ouzounis Christos

Publications

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

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

Danko D., et. al

A global metagenomic map of urban microbiomes and antimicrobial resistance

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

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

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

Critical assessment of protein intrinsic disorder prediction

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

Kyritsis K., Ouzounis C., Angelis L., Vizirianakis I.

Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

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

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

Danko D., et. al

A global metagenomic map of urban microbiomes and antimicrobial resistance

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

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

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

Critical assessment of protein intrinsic disorder prediction

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

Kyritsis K., Ouzounis C., Angelis L., Vizirianakis I.

Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

Chasapi A., Promponas V., Ouzounis C.

The bioinformatics wealth of nations

Karapiperis C., Chasapi A., Angelis L., Scouras Z., Mastroberardino P., Tapio S., Atkinson M., Ouzounis C.

The coming of age for Big Data in Systems Radiobiology, an engineering perspective

Quick Links

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Ptolemaida

Athens

BCPL –
Biological Computation & Process Laboratory