Matteo Ferla has experience in bioinformatics analysis, genome mining, protein engineering, and experimental design. He received his PhD from the University of Otago, where he investigated catalytic multitasking in enzymes. Currently he works as a data analyst and protein engineer at Biosyntia ApS, analyzing genomes and proteomics datasets to improve production strains. He has strong programming skills and experience analyzing NGS data, performing phylogenetics, and manipulating bacterial genomes.
James J. Collins
Howard Hughes Medical Institute
Dept of Biomedical Engineering & Center of Synthetic Biology
Boston University
Wyss Institute for Biologically Inspired Engineering
Harvard University
KnetMiner provides an easy to use web interface to visualisation and data mining tools for the discovery and evaluation of candidate genes from large scale integrations of public and private data sets. It addresses the needs of scientists who generally lack the time and technical expertise to review all relevant information available in the literature, from key model species and from a potentially wide range of related biological databases. We have previously developed genome-scale knowledge networks (GSKNs) for multiple crop and animal species (Hassani-Pak et al. 2016). The KnetMiner web server searches and evaluates millions of relations and concepts within the GSKNs in real-time to determine if direct or indirect links between genes and trait-based keywords can be established. KnetMiner accepts as user inputs: search terms in combination with a gene list and/or genomic regions. It produces a table of ranked candidate genes and allows users to explore the output in interactive genome and network map visualisation tools that have been optimised for web use on desktop and mobile devices. The KnetMiner web server and the GSKNs provide a step-forward towards systematic and evidence-based gene discovery.
James J. Collins
Howard Hughes Medical Institute
Dept of Biomedical Engineering & Center of Synthetic Biology
Boston University
Wyss Institute for Biologically Inspired Engineering
Harvard University
KnetMiner provides an easy to use web interface to visualisation and data mining tools for the discovery and evaluation of candidate genes from large scale integrations of public and private data sets. It addresses the needs of scientists who generally lack the time and technical expertise to review all relevant information available in the literature, from key model species and from a potentially wide range of related biological databases. We have previously developed genome-scale knowledge networks (GSKNs) for multiple crop and animal species (Hassani-Pak et al. 2016). The KnetMiner web server searches and evaluates millions of relations and concepts within the GSKNs in real-time to determine if direct or indirect links between genes and trait-based keywords can be established. KnetMiner accepts as user inputs: search terms in combination with a gene list and/or genomic regions. It produces a table of ranked candidate genes and allows users to explore the output in interactive genome and network map visualisation tools that have been optimised for web use on desktop and mobile devices. The KnetMiner web server and the GSKNs provide a step-forward towards systematic and evidence-based gene discovery.
Applications of bioinformatics, main by kk sahuKAUSHAL SAHU
Introduction
Goals of Bioinformatics
Bioinformatics & Human Genome
Project
What can we do using bioinformatics ?
Applications of bioinformatics in various fields
1) Medicine
2) Evolutionary studies
3) Agriculture
4) Microbiology
5) Biotechnology
Conclusion
References
WHAT IS BIOINFORMATICS?
Computational Biology/Bioinformatics is the application of computer sciences and allied technologies to answer the questions of Biologists, about the mysteries of life. It has evolved to serve as the bridge between:
Observations (data) in diverse biologically-related disciplines and
The derivations of understanding (information)
APPLICATIONS OF BIOINFORMATICS
Computer Aided Drug Design
Microarray Bioinformatics
Proteomics
Genomics
Biological Databases
Phylogenetics
Systems Biology
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
Applications of bioinformatics, main by kk sahuKAUSHAL SAHU
Introduction
Goals of Bioinformatics
Bioinformatics & Human Genome
Project
What can we do using bioinformatics ?
Applications of bioinformatics in various fields
1) Medicine
2) Evolutionary studies
3) Agriculture
4) Microbiology
5) Biotechnology
Conclusion
References
WHAT IS BIOINFORMATICS?
Computational Biology/Bioinformatics is the application of computer sciences and allied technologies to answer the questions of Biologists, about the mysteries of life. It has evolved to serve as the bridge between:
Observations (data) in diverse biologically-related disciplines and
The derivations of understanding (information)
APPLICATIONS OF BIOINFORMATICS
Computer Aided Drug Design
Microarray Bioinformatics
Proteomics
Genomics
Biological Databases
Phylogenetics
Systems Biology
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Bioinformatics in biotechnology by kk sahu KAUSHAL SAHU
Introduction
Bioinformatics – definition
History
Required skills
Core areas of bioinformatics
Components of bioinformatics
Nomenclature system in bioinformatics
Biological databases
Types of database
Bioinformatics tools
Applications of bioinformatics
Conclusion
References
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
XIi CONGRESO INTERNACIONAL DE SECRETARIASMelissa Soria
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Logistics management is the part of supply chain management that plans, implements, and controls the efficient, effective forward, and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customer's requirements
1. Matteo Ferla
Ribisvej 3, Gentofte, Denmark matteo.ferla@gmail.com 0044 7548849836
Contact Details
Links
Research Associate
July 2015 – October 2015 Institution: University of Bath
I was contracted by Prof. David Leak to analyse RNAseq data from Geobacillus thermoglucosida-
sius, set up an analysis pipeline and to teach his students. The task also involved extensive clean-
ing of poor genomic annotations. Lastly, in ordert to increase outreach of the research I built a
database website (http://www.geobacillus.com).
August 2014 – May 2015 Institution: University of Otago
I have done contracted bioinformatics jobs for two groups from my former department involv-
ing genome data mining (Dr. Alan Carne), molecular dynamics simulations and Matlab coding
(Dr. Sigurd Wilbanks) and various minor phylogenetic jobs for various researchers.
Research Scientist
November 2015 – Present Company: Biosyntia ApS
My major roles are as data analyst and as protein engineer. My tasks include extensive genome
mining, analysis of protemics and other complex large datasets (in order to devise changes to
improve the production strain), troubleshooting and optimisation of fluorescent genetic
biosensors, detailed experimental design for delegation, and scripting for Google Sheets, for
task automation and for data intregration.
Indicative Skills
▶ Exceptional understanding and insight into bacterial metabolic logic and chemistry
▶ NGS data analysis and genome data mining
▶ Phylogenetics, including ancestral sequence reconstruction
▶ Programming in Python, JavaScript and Perl
▶ MatLab, R, MS Office, Photoshop, Illustrator and Dreamweaver
▶ Web depelopment both serverside and clientside
▶ Familiarity with Unix environment and cluster computing
▶ General molecular biology wetlab skills
▶ Optimisation of protein expression and purification
▶ Biochemical analytical methods
▶ Enzyme mutagenesis
▶ E. coli genome manipulation (incl. MAGE)
GitHub https://github.com/matteoferla
A multitude of projects I have written throughout the years.
Science blog http://blog.matteoferla.com
My thought, analyses and findings about synthetic biology and biochemistry.
Homepage http://www.matteoferla.com
Links to several resources written by me.
2. MBiol (Hons) in Molecular Biology
Sept. 2004–July 2008 Mark: First class Institution: University of Bath
This was a combined Bachelors and Masters degree with a one-year internship.
Placement supervisor: Dr. Craig Tomlinson, Microarray Core, Dartmouth College (USA)
Role: I worked with mammalian cell lines, RNA, radioactivity and DNA hybridisations in
order to develop a technique to measure whole genome transcription rates.
Thesis supervisor: Dr. Matthew Wills, Department of Biology & Biochemistry, University of
Bath
Thesis: The phylogeny of the Malacostraca.
This resulted in a middle-author paper and the Oxford University Press prize in bioscience (Uni-
versity of Bath), 2008 award for best thesis in class.
Additional Research Experience
Sept. 2008–Dec. 2009 Supervisor: Prof. John Pickup Institution: King’s College London
I expressed, purified, fluorescently labelled and assayed glucose-binding protein mutants. I also
functionalised the ends of fibre optic cables with PEG hydrogels. This resulted in a middle-au-
thor paper.
PhD in Biochemistry
Febuary 2010 – August 2014 Institution: University of Otago
Supervisor: Dr. Wayne M. Patrick
Thesis: Catalytic multitasking in MetC: one enzyme, multiple reactions
(https://otago.ourarchive.ac.nz/handle/10523/4911)
Main project: I combined enzymology, bioinformatics and phylogenetics in order to identify and
investigate a set of multifunctional enzymes.
Side projects:
▶ Investigation of the phylogenetics of the Alphaproteobacteria.
▶ Collection of phylogenetic data for a paper in collaboration with Dr. Monica Gerth.
▶ Gaining an in-depth knowledge of microbial metabolism in order to investigate the
metabolism of certain species.
Ferla M.P., Mutanalyst, an online tool for assessing the mutational spectrum of epPCR
libraries with poor sampling. BMC bioinformatics, 2016, (17)152 (DOI:
10.1186/s12859-016-0996-7)
Details: Single author paper describing an online tool I developed indepedently:
http://www.mutanalyst.com. Journal impact factor: 2.6.
Ferla M.P., Patrick W.M., Bacterial methionine biosynthesis. Microbiology, 2014
160(8):1571–84. (DOI: 10.1099/mic.0.077826-0)
Details: A review of the litterature along with genome analyses in order to determine
pathway distribution. Accepted with no revisions. Cited twice. Journal impact
factor: 2.8.
Ferla M.P., Thrash J.C., Giovannoni S.J., Patrick W.M., New rRNA gene-based phylogenies of
the Alphaproteobacteria provide perspective on major groups, mitochondrial ancestry and
phylogenetic instability. PLoSOne, 2013, 8(12):e83383. (DOI: 10.1371/journal.pone.0083383)
Details: The result of a collaboration with Prof. Giovannoni that I spearheaded. Several
different analyses were used to exclude the possibility of AT-richness driven
bias. Cited ten times, including by the latest edition of The Prokaryotes. Journal
impact factor: 3.5.
Publications
3. Gerth M.L., Ferla M.P., Rainey P.B., The origin and ecological significance of multiple
branches for histidine utilization in Pseudomonas aeruginosa PAO1. Environ Microbiol. 2012,
14(8): 1929–40. (DOI: 10.1111/j.1462-2920.2011.02691.x)
Details: I performed the phylogenetic analyses to infer the evolutionary history of the
different branches of the pathway. Cited twice. Journal impact factor: 6.2.
Saxl T., Khan F., Ferla M.P., Birch D., Pickup J., A fluorescence lifetime-based fibre-optic
glucose sensor using glucose/galactose-binding protein. Analyst. 2011, 136(5): 968–72. (DOI:
10.1039/c0an00430h)
Details: I purified protein and performed chemical derivatisation. Cited 21 times.
Journal impact factor: 3.9.
Jenner, R.A., Nidhubhghaill, C., Ferla, M.P., Wills, M.A., Eumalacostracan phylogeny and
total evidence: limitations of the usual suspects. BMC Evol Biol, 2009, 9(1):21. (DOI:
10.1186/1471-2148-9-21)
Details: Some of the datasets and the resulting trees from my undergraduate thesis were
used in the paper. Cited 34 times. Journal impact factor: 3.4.
Publications (cont’d)
Ferla M.P., Hall K.R., Yosaatmadja Y., Comer N., Squire C., Patrick W.M., Phyloenzymology
uncovers primordial-like enzymes in bacteria with streamlined genomes.
Planned journal: Mol. Biol. Evol.
Details: This manuscript requires a prior lab paper to be submitted. It is based on my
main PhD project.
Ha M., Ferla M.P., McConnell M.A, Bekhit A.E.A., Carne A., In-depth characterization
of ruminant milk whey proteomes from biological and evolutionary
perspectives. Planned journal: Journal of proteomics
Details: I performed the data analysis comparison of the tissue-specific proteomes of the
three species investigated.
Future publications
Gronenberg L., Salomonsen B., Ferla M.P., Genee H.J., A genetically modified bacterial cell
factory for thiamine production, 2015, EU patent (15201200.1)
Patent
Oral presentations
Catalytic multitasking in MetC: one enzyme, multiple reactions. departmental seminar,
Department of Biochemistry. University of Otago, 2014.
MetC: A multitasking enzyme in three bacteria with streamlined genomes. Queenstown
Molecular Biology Meeting, Enzyme Evolution and Engineering Session, 2013.
The rise and fall of enzyme activities. Otago School Medical Sciences Postgraduate Symposium,
University of Otago, 2013.
Promiscuity, minimalism and redundancy. Institute of Natural Sciences Postgraduate
Symposium, Massey University (Auckland), 2012.
4. Dr. Wayne Patrick
Senior lecturer and PhD supervisor
Department of Biochemistry, University of Otago, Dunedin, NZ
wayne.patrick@otago.ac.nz
+64 3 479-7897
Prof. Kurt Krause
Head of Department during my PhD and thesis committee member
Department of Biochemistry, University of Otago, Dunedin, NZ
kurt.krause@otago.ac.nz
+64 3 479-5166
Dr. Monica Gerth
Research fellow and collaborator
Department of Biochemistry, University of Otago, Dunedin, NZ
monica.gerth@otago.ac.nz
+64 3 479-7836
Referees
Posters
Ferla M.P. and Patrick W.M. The rise and fall of enzyme activities. Presented at both Society
for Molecular Biology and Evolution Meeting 2012 (Dublin) and Gordon Research Conference
Biocatalysis 2012 (Rhode Island).
Ferla M.P. and Patrick W.M. Searching for prokaryotic amino acid sequences with too many
tandem repeats. NZ Structural Biology Meeting 2010 (Waiheke island, NZ).
Services
▶ Reviewer for the African Journal of Biotechnology and Nucleic Acid Research.
▶ Coordinator for the social club of University of Otago Deparment of Biochemistry for
two years (fortnightly event organisation, financial accounting, publicity etc.)