First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP3058337, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
IF1839, Degree course structure A.Y. 2011/12, A.Y. 2019/20
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Number of ECTS credits allocated 7.0
Type of assessment Mark
Website of the academic structure
Department of reference Department of Biology
Mandatory attendance
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge FRANCESCO FILIPPINI BIO/11

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines BIO/11 Molecular Biology 5.0
Other -- -- 2.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Practice 2.0 32 18.0 No turn
Laboratory 0.5 8 4.5 No turn
Lecture 4.5 36 76.5 No turn

Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
4 INFORMATICA E BIOINFORMATICA 2018-2019 01/10/2018 30/11/2019 FILIPPINI FRANCESCO (Presidente)

Prerequisites: Bioinformatics manages and analyzes DNA and protein sequences, genomic, proteomic and metabolomic data, protein structures, functional sinatures etc. Therefore, basic knowledge in Molecular Biology and Biochemistry is crucial for properly following the Bionformatics course. In particular, knowledge is needed about nature and structure of DNA, RNA, proteins, gene structure and function, transcriptional regulation and splicing, proteins, protein domains and catalytic, regulatory and binding sites, cloning and expression, sequencing of genomes and transcriptomes.
Target skills and knowledge: This course consists of two parts: the first one (Part A) is addressed to all 3rd-year students, while the second part (Part B) only concerns students from the molecular, cellular and environmental curriculum or those ones having chosen to attend the whole course. Therefore, knowledge and skills are separately presented as follows.

PART A (common)
1 - to be fully aware about the relevance, in a Biotechnological context, of Informatics and Bioinformatics for proper management and analysis of bio-information;
2 - to know main bio-databases and their framework and to be able to retrieve relevant and specific information;
3 - to know sequence comparison by alignment, BLAST and its main applications; to be able to use such tools for functional inference and in silico dissection;
4 - to know and properly use regular expressions and profiles as functional markers to integrate data mining and homology searches;
5 - to know structural databases and to be able to view structures and highlight details by using molecular viewers.

PART B (specific)
1 - to know local and global alignment methods and matrix based scoring systems;
2 - to be able to use as 'advanced users' alignment methods, by knowledge-based modification of software presets for analysis iteration and tuning;
3 - to know, use and even develop all different kinds of functional markers (in protein or DNA sequences) and their precision and recall indexes;
4 - to know structural databases and to be able to perform - in addition to structure details viewing - structure comparison; to be able to predict secondary structure and to interpretate prediction output, to know (basics) 3D structure prediction methods;
5 - to know methods for interpretating a genome or transcriptome draft and problems concerning gene finding and annotation in prokarya and eukarya;
6 - to combine aforementioned approaches in integrative functional inference, as well for "smart design" of wet lab experiments and biotech engineering projects.
Examination methods: According to previously explained division of the course in two parts (A and B) and of the students as well (a complete cohort and a partial one) details on exams are presented as follows.

PART A (common)
The evaluation of knowledge and skills occurs both along the course and after its end; in particular, students in the laboratory classroom, after an interactive training step build up - as team work and by interacting with the teacher and each other - a "mini-review & analysis" re. gene(s)/protein(s) of biotech interest, combining data mining and integrative analyses. The teacher provides students with peer feedback for incremental improvements of theis work. Theoretical knowledge is evaluated by oral exam in which the student is requested to present methods in the context of example, real or putative projects, in order knowledge is functional (and linked) to problem solving skills. All students can take advantage of scientific journal articles on bioinformatics and biotech topics for presenting studied methods in real R&D context.
Positive results of exams for this common part are registered (just ability, without vote) only for those students who do not attend the whole course.

PART B (specific)
The evaluation of knowledge and skills occurs also for this part along the course and after its end. In the computer classroom, students build up a number of (question driven) written reports (with open responses and comments), each concerning a main topic in the course program. Along the creation and improvement of such reports, students interact each other and with the teacher and are provided with peer feedback aimed at stimulating them to improve the reports and their presentation. Reports from the computer classroom determine the first half of the vote (15/30) + eventual bonus for brilliant reports. Theoretical knowledge is evaluated by oral exam, in which methods are linked to activities performed in the computer classroom, as well as to scientific articles selected by the students and to putative projects proposed by the student and/or the teacher. The oral part of the exam contributes the second half of the vote and the students are suggested by peer feedback to further improve, when needed, those reports or program parts under the average score of the exam.
Assessment criteria: According to the theoretical and practical nature of this course, evaluation will take into account acquisition of both knowledge and problem solving skills.

Concerning practical skills, evaluation will focus on:
- problem solving skills shown along with activities in the computer classroom, i.e. capacity to properly use and integrate remote resources (softwares e databases), showing awareness about their potential and limits;
- congruence and completeness in responses to driving questions for building up the 'test' reports associated to computer classroom activities;
- capacity to focus on elements crucial for inferring relevant information;
- capacity to identify soon best predictive indications;
- capacity to present data and analyses rigorously and with complete and clear format;
- capacity to take advantage from feedback for incremental improvement of analysis and report presentastion;
- capacity to proactive action along with team work activities and proper integration of her/his own contribution with those from other team members.

Concerning theoretical knowledge, evaluation will focus on:
- knowledge of structure and organisation of remote resources (databases, portals) presentated in this teaching course;
- knowledge of methods underlying function of analytical tools (software for comparison, analysis and prediction) presentated in this teaching course;
- knowledge of both potential and limits of such resources and methods;
- knowledge of best strategies for combined and integrated use of such resources and methods along with analyses.

The evaluation process aims to stimulate self-evaluation and self-improvement rather than to just provide a 'judgement'. Therefore, during the different evaluation process steps, the student is given the possibility to rescue eventual gaps taking advantage of feedback from the teacher and of partial rescue/incremental improvement activities.
Course unit contents: PART A (common):
Lesson topics:
Bioinformatics in the context of Biotechnology and Molecular Biology. Biomedical and bioinformatic databases of interest to Biotechnologies. Bio-data management. Crucial elements in databases: structural order, entries/records, fields and levels. Cross-refs in databases. Main international databases and bioinformatic organizations. Simple and advanced queries, boolean combinations, NCBI and EBI search tools. Basic knowledge about sequence comparison by alignment; main BLAST applications. Regular expressions (patterns) and profiles as functional markers for domains, motifs, sites, signatures. Secondary structure prediction. Structure based alignments. Validation of functional markers. Protein structures: PDB files and their viewing and simple analysis with UCSF Chimera.
Two activities will be performed in the PC classroom, i.e. a training stage followed by a test and team work activity:
TRAINING: integrative search for information about an example protein (and related coding gene and alternative transcripts) by data mining of bibliographic databases, analysis of sequence database entries, and analysis by alignment, functional markers and structure viewing.
TEST: the training activity is followed to write as a team work a mini-review & analisis concerning a gene/protein/pathway of interest (free choice)

PART B (specific)
Lesson topics:
Alignment of DNA and protein sequences: potential, limits and correct analysis of results. Similarity evaluation criteria. Globale and local alignment. Scoring systems. Matrices: "dot plot", PAM and BLOSUM. The BLAST algorithm and its basic and special applications. How to select a method based and how to interpretate results. Search tuning by settings modification and search iterations. Filters and output options. Multiple alignment as a tool for identifying consensus sequences. Sequence tags in protein sequences: regular expressions and profiles. Repeat regions: biological relevance of frequence and spreading. Pattern scanning in proteins. PROSITE. Precisione and recall indexes. Advanced use of secondary structure predictors and conformational transitions. Structure-based alignments and sequence tags. Structural databases and entries: PDB. Structure comparison by superposition. Search by fold conservation: DALI. 3D structure prediction and main methods for building models. Promoter patterns in DNA sequences and identification of regulatory regions. Regulatory networks and co-regulation. miRNA and miRNA target prediction.
Interpretation of a genome draft. EST clustering. ORF finding and gene finding in Prokarya and Eukarya. Genome annotation.
Examples of reasearch articles reporting on computer-aided biotechnology.
Computer classroom activities (alternating training and test phases) focus on using main publicly available tools for similarity searches (BLAST family applications), regular expression and profile scanning (ScanProsite, PROscan), structural bioinformatics and on their integration as analysis and prediction tools.
Planned learning activities and teaching methods: Learning will take advantage from lessons, interaction among the teacher and the students, as well as from available didactic material.
In the first meeting with students, full details are presented concerning course topics and teaching/learning approach, as well as on available online resources.
Part A (common) is based on both lessons and practical data mining and simple sequence analysis in the computer classroom, followed by a team work providing students with hints for writing of a scientific article of review & analysis type. Students are suggested to coordinate their activities in the team in order they would learn how to integrate information to build up a short manuscript showing completeness, rigorous and well presented contents. To this aim, peer feedback from the teacher, in the classroom as well as via remote communication, provides students with indications and suggestions.
Part B (specific) is also based on interactive lessons and on "problem solving" activities in the computer clasroom, consisting of training activities followed by a test stage. In performing test stage, students follow indications and respond to challenge-questions asking them to use online available bioinformatic tools for funcional inference analyses simulating real biotechnological projects. The teacher provides peer feedback about the report and links problem solving practical activities to lesson topics, interacting with the students.
During the course team work occurs, with results comparison, and case studies are suggested by the teacher as well as by the students. Pre-exam training will also occur, including examples of questions and how to properly formulate responses, as well as evaluation criteria.
Additional notes about suggested reading: Resources for the students are located at a web site created by the teacher for this course and updated every year, as well during the course activities. Such a website contains - as free download pdf files - the complete material concerning lessons and theoretical knowledge, as well pages with the online guide to all computer classroom activities, the course schedule, links to remote resources, eventual warning messages.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Questioning
  • Problem solving
  • Peer feedback
  • Loading of files and pages (web pages, Moodle, ...)
  • web pages for this course

Innovative teaching methods: Software or applications used
  • integrative approach using remote software

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