E-Lab Notebook Entry #1 is d ue Thursday, August 28

1. E-Lab Notebook Entry #1 is due Thursday, August 28 http://bio3170bioinformatics.wordpress.com/category/e-notebook-entry-1/

2. E-Notebook Entry example is online Gene alignments: antibiotic resistance Protein alignments: function of CFTR protein Exploring bioinformatics: Nova Scotia Niemann-Pick Disease Manipulating genetic data: genetic disease Sequence assembly: human genome Bioinformatics and Computational Biology (BCB) Agent-based models: foraging ants Gene prediction: prokaryotic gene discovery Mathematical models: infectious disease Microarrays: zinc-induced gene expression Structure prediction: drug design

3. The learning objectives for module 1 relate to manipulating genetic data Manipulating genetic data: genetic disease • Investigate Python’s interactive shell • Formulate an algorithm to find the complement of a DNA strand • Find and manipulate genetic data using web-based tools • Devise and implement an algorithm in Python to transcribe a strand of DNA Sickle Cell Anemia http://my.clevelandclinic.org/disorders/sickle_cell_anemia/hic_sickle_cell_anemia.aspx

4. Python’s interactive shell allows you write code and see the results in real time • Investigate Python’s interactive shell • Formulate an algorithm to find the complement of a DNA strand • Find and manipulate genetic data using web-based tools • Devise and implement an algorithm in Python to transcribe a strand of DNA http://bio3170bioinformatics.wordpress.com/syllabus/  Schedule  Intro to Python OR http://bio3170bioinformatics.files.wordpress.com/2014/08/introduction_to_python.pdf

5. Before addressing DNA, how might we go about finding a friend’s phone number in a phone book? • Investigate Python’s interactive shell • Formulate an algorithm to find the complement of a DNA strand • Find and manipulate genetic data using web-based tools • Devise and implement an algorithm in Python to transcribe a strand of DNA http://ktmb.org/phone-book-recycling/ Sequential search: step through each element, one at a time Binary search: reduce the number of remaining elements by half with each iteration As the phone book size increases, the binary search will outperform sequential So how about finding the complement of a DNA strand?

6. Working with sequence data is a primary task of many bioinformaticians • Investigate Python’s interactive shell • Formulate an algorithm to find the complement of a DNA strand • Find and manipulate genetic data using web-based tools • Devise and implement an algorithm in Python to transcribe a strand of DNA : Whole Genome Sequences http://www.ncbi.nlm.nih.gov/genbank/statistics http://bio3170bioinformatics.wordpress.com/syllabus/  Schedule  Entry #2: Genetic Disease OR http://bio3170bioinformatics.files.wordpress.com/2014/08/module_1_genetic_disease.pdf

7. We can use computers to do many of the sequence analysis tasks that are tedius and extremely difficult to do by hand. • Investigate Python’s interactive shell • Formulate an algorithm to find the complement of a DNA strand • Find and manipulate genetic data using web-based tools • Devise and implement an algorithm in Python to transcribe a strand of DNA http://www.chemguide.co.uk/organicprops/aminoacids/dna3.html http://bio3170bioinformatics.wordpress.com/syllabus/  Schedule  Transcribing DNA in Python OR http://bio3170bioinformatics.files.wordpress.com/2014/08/transcribing-dna1.pdf