Course Information

IV108 Bioinformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
In-person direct teaching

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 25. 9. to Wed 18. 12. Wed 12:00–13:50 A219

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 36 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bioinformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 12:00–13:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 85 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bioinformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 18:00–19:50 A215

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 85 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bioinformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 13. 9. to Mon 6. 12. Mon 18:00–19:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 85 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 14:00–15:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 85 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. Ing. Matej Lexa, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 12:00–13:50 B117

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 85 fields of study the course is directly associated with, display

Course objectives

Introduction to selected algorithms and methods of analysis used in bioinformatics.

Learning outcomes

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 16:00–17:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 48 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 10:00–11:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 48 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 18:00–19:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 48 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 10:00–11:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 48 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 12:00–13:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 47 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Wed 8:00–9:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 47 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods and data processing
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 9:00–9:50 B411

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 47 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 17:00–17:50 B411

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 47 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Thu 15:00–15:50 B411, Thu 16:00–16:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 45 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 8:00–8:50 B411, Tue 9:00–9:50 B117

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 45 fields of study the course is directly associated with, display

Course objectives

At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3-D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods

Syllabus

• Algorithms for sequence analysis
• Algorithms for prediction and analysis of structural data
• Biological language
• Next-generation DNA sequencing methods
• Understanding protein cleavage and mass spectra
• Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Teaching methods

lectures and exercises

Assessment methods

Bonus exercices, final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis
• IV116 Applied bioinformatics: The Evolutionary relationships of genes and species

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 9:00–9:50 B410, Tue 9:00–10:50 B117

Prerequisites

IV107 Bioinformatics I or consent of the teacher (not needed for biology students).

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 38 fields of study the course is directly associated with, display

Course objectives

Advanced bioinformatics course with explanations of the inner workings of selected algorithms, their advantages and disadvanteges. Recent alternatives to common practices will be presented. The students should learn to evaluate or design methods for solving current problems in bioinformatics. Includes writing own code in the practical part of the course.

Syllabus

• 1. Algorithms for sequence analysis 2. Algorithms for prediction and analysis of structural data 3. Biological language 4. Next-generation DNA sequencing methods 5. Understanding protein cleavage and mass spectra 6. Expression profile and promoter analysis

Literature

• ZVELEBIL, Marketa J. and Jeremy O. BAUM. Understanding bioinformatics. New York, N.Y.: Garland Science, 2008, xxiii, 772. ISBN 9780815340249. info
• JONES, Neil C. and Pavel PEVZNER. An introduction to bioinformatics algorithms. Cambridge, Mass.: MIT Press, 2004, xviii, 435. ISBN 0262101068. info

Assessment methods

Bonus exercices , final exam.

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 8:00–8:50 B204, Tue 9:00–9:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher for other than biology students.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 38 fields of study the course is directly associated with, display

Course objectives

Advanced bioinformatics with explanations of the inner working of selected algorithms, their advantages and disadvanteges. Recent alternatives to common practices will be presented. The students should learn to evaluate or design methods for solving current problems in bioinformatics. They will write their own code in the practical part of the course.

Syllabus

• 1. Algorithms for sequence analysis 2. Algorithms for prediction and analysis of structural data 3. Biological language 4. Understanding protein cleavage and mass spectra 5. Expression profile and promoter analysis

Literature

• WATERMAN, Michael. Introduction to computational biology. Maps, sequences and genomes. Boca Raton: CRC Press, 1995, 431 pp. ISBN 0-412-99391-0. info

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 12:00–13:50 B116, Tue 12:00–13:50 B204

Prerequisites

IV107 Bioinformatics I or consent of the teacher for other than biology students.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 18 fields of study the course is directly associated with, display

Course objectives

Advanced bioinformatics with explanations of the inner working of selected algorithms, their advantages and disadvanteges. Recent alternatives to common practices will be presented. The students should learn to evaluate or design methods for solving current problems in bioinformatics. They will write their own code in the practical part of the course.

Syllabus

• 1. Algorithms for sequence analysis 2. Algorithms for prediction and analysis of structural data 3. Biological language 4. Understanding protein cleavage and mass spectra 5. Expression profile and promoter analysis

Literature

• WATERMAN, Michael. Introduction to computational biology. Maps, sequences and genomes. Boca Raton: CRC Press, 1995, 431 pp. ISBN 0-412-99391-0. info

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar
• IV110 Project in Sequence Analysis

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Tue 8:00–8:50 B204, Tue 9:00–9:50 B116

Prerequisites

IV107 Bioinformatics I or consent of the teacher for other than biology students.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 18 fields of study the course is directly associated with, display

Course objectives

Advanced bioinformatics with explanations of the inner working of selected algorithms, their advantages and disadvanteges. Recent alternatives to common practices will be presented. The students should learn to evaluate or design methods for solving current problems in bioinformatics. They will write their own code in the practical part of the course.

Syllabus

• 1. Algorithms for sequence analysis 2. Algorithms for prediction and analysis of structural data 3. Biological language 4. Understanding protein cleavage and mass spectra 5. Expression profile and promoter analysis

Language of instruction

Czech

Follow-Up Courses

• IV105 Bionformatics seminar
• IV106 Bioinformatics seminar

Further Comments

The course is taught annually.

Teacher's information

http://www.fi.muni.cz/~lexa/teaching.html

IV108 Bionformatics II

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

doc. Ing. Matej Lexa, Ph.D. (lecturer)

Guaranteed by

prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 18 fields of study the course is directly associated with, display

Language of instruction

Czech

Further Comments

The course is taught annually.
The course is taught: every week.

• Enrolment Statistics (recent)