Lecturer: Prof. Nadia Pisanti
[February 8th] This page has been created!
Regular live classes are suspended from March 5th, 2020
Classes will thus take place here
Prof. Pisanti makes office hours on skype or meet upon appointment arranged by mail.
This course has the goal to give the student an overview of algorithmic methods that have been conceived for the analysis of genomic sequences. We will focus both on theoretical and combinatorial aspects as well as on practical issues such as whole genomes sequencing, sequences alignments, the inference of repeated patterns and of long approximated repetitions, the computation of genomic distances, and several biologically relevant problems for the management and investigation of genomic data. The exam (see below for its description) has the goal to evaluate the students understanding of the problems and the methods described in the course. Moreover, the exam is additionally meant as a chance to learn how a scientific paper is like, and how to make an oral presentation on scientific/technical topics, that is designed for a specific audience.
A brief introduction to molecular biology: DNA, proteins, the cell, the synthesis of a protein.
Sequences Alignments: Dynamic Programming methods for local, global, and semi-local alignments. Computing the Longest Common Subsequences. Multiple Alignments.
Pattern Matching: Exact Pattern Matching: algorithms (Knuth-)Morris-Pratt, Boyer-Moore, Karp-Rabin with preprocessing of the pattern. Algorithm with preprocessing of the text: use of indexes. Motifs Extraction: KMR Algorithm for the extraction of exact motifs and its modifications for the inference of approximate motifs.
Finding Repetitions: Algorithms for the inference of long approximate repetitions. Filters for preprocessing.
Fragment Assembly: Genomes sequencing: some history, scientific opportunities, and practical problems. Some possible approaches for the problem of assembling sequenced fragments. Link with the “Shortest common superstring” problem, the Greedy solution. Data structures for representing and searching sequencing data.
New Generation Sequencing: Applications of High Throughput Sequencing and its algorithmic problems and challenges. Investigating data types resulting from the existing biotechnologies, and the possible data structures and algorithms for their storage and analysis.
A basic course on algorithmic.
SEQUENCES ALIGNMENTS Alignments
PATTERN MATCHING ExactPatternMatching e KnuthMorrisPratt&BoyerMoore KarpRabin87
SUFFIX TREE SuffixTrees
EXACT MOTIFS EXTRACTION KarpMillerRosenberg
APPROXIMATE MOTIFS EXTRACTION KMRC Speller StructuredMotifs
FINDING REPETITIONS: FILTERING TUIUIU
FRAGMENT ASSEMBLY FragmentAssembly
NEW GENERATION SEQUENCING NewGenerationSequencing
OVERVIEW OF SEQUENCING TECNOLOGIES SequencingTechnologies
HAPLOTYPE ASSEMBLY WhatsHap
BUBBLES IN DE BRUIJN GRAPHS (slides) Bubbles
Each student will have to undergo an oral exam over the topics of the classes. Moreover, each student is assigned a paper that is a very recent scientific work on topics related to those of the course (tipically it is a paper accepted for publication in the proceedings of an international conference that is going to be held in a few weeks/months). The paper is part of a pool of possible papers selected by the lecturer. The paper assignment follows a brief description of all papers in the pool made by the lecturer, and a bidding phase of the students over such papers. Once the student has his/her paper assigned, the task is to prepare and make a presentation of that work that: (1) describes the results presented in that paper, (2) is suited for the actual audience (that will be the course class) as for comprehension opportunity, (3) sticks to the allowed time slot.
Students presentations usually take place all together somewhen at the end of the course. Exceptions are possible upon request for specific needs. Once the course is over, students can undergo the examination anytime during the academic year by agreeing an appointment: please, send an email to the teacher.
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