Lecturer: Nadia Pisanti
[April 3rd] The lecture of Monday April 24th will not take place.
[March 9th] The papers assignment for the final exam will take place during the lecture of March 30th.
[February 27th] As we agreed, on Monday's the class will start at 14:30. Hence, the class will be 14:30-16:00 with no break.
[February 6th] The first lecture will be on Monday February 20th in room L1.
[February 6th, 2017] (This) Web page for academic year 2016-2017 is created!
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 extracion 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.
SUFFIX TREE tre.pdf
PATTERN MATCHING patternmatching1.pdf e patternmatching2.pdf
FRAGMENT ASSEMBLY fragmentassembly.pdf
SEQUENCES ALIGNMENTS allineamenti.pdf
FINDING REPETITIONS: FILTERING amb.pdf
NEW GENERATION SEQUENCING illumina-assembly.pdf
OVERVIEW OF SEQUENCING TECNOLOGIES en104-pisanti.pdf
BUBBLES IN DE BRUIJN GRAPHS (slides) seminar-bubbles.pdf
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.
Giulio Del Corso. “Dynamic Alignment-free and Reference-free Read Compression”
Andrea Madotto. “AllSome Sequence Bloom Tree”
Lucio Messina. “Optimal Omnitig Listing for Safe and Complete Contig Assembly”
Federico Umani: “On-line pattern matching on similar texts”
Matteo Loporchio. “Joker de Bruijn: sequence libraries to cover all k-mers using joker characters”
Sabrina Briganti. “Superbubbles, Ultrabubbles and Cacti”
Mario Leonardo Salinas. “The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-Sample Bulk Sequencing Tumor Data”