Download BINF6201/8201: Molecular Sequence Analysis

BINF6201/8201: Molecular Sequence Analysis
Dr. Zhengchang Su
Office: 351 Bioinformatics Building
Email: [email protected]
Office hours: Tuesday and Thursday: 2:00~3:00pm
08-23-2016
Textbook and reading/reference materials
Ø  Textbook: Bioinformatics and Molecular Evolution
by Paul G. Higgins and Teresa K. Attwood,
Blackwell Publishing, 2005.
Relevant materials: http://www.wiley.com/WileyCDA/WileyTitle/
productCd-1405106832.html
Ø  Reference textbooks:
1.  Molecular Evolution, Wen-Hsiung Li, Sinauer Association
2.  Biological Sequence Analysis, Richard Durbin et. al. Cambridge Univ. Press
3.  Introduction to Linear Algebra, 5th Ed. Gilbert Stanger, Wellesley-Cambridge
Press
4.  Statistical Methods in Bioinformatics, 2th Ed. Warren J. Ewens, Springer
5.  Deep Learning, Ian Goodfellow, et. al. The MIT Press
Ø  Additional readings from the current literature may be assigned as
appropriate
Ø  Lecture slides and lab materials and instructions: http://
bioinfo.uncc.edu/zhx/binf8201/binf8201.html
Students Evaluation
Ø Weekly or bi-weekly homework assignments, Ph.D students
may have additional assignments (20%).
Ø Two midterm exams (60%):
10/6(Thursday) and
12/13 (Tuesday)
Ø Classroom participation will count for 5% of the grade.
Ø Lab participation will count for 5% of the grade.
Development of DNA sequencing technologies
Ø  Chain termination (Sanger) method (1977): read length 800~1000 bps
Ø  Automation of sequence determination (late 1980s)
Ø  Shotgun whole genome sequencing strategy (1995)
Ø Massively parallel (Next generation (NexGen)) sequencing
technologies (2004): read length: 300bps, ~1010 bps/day/machine
1. 454 pyrosequencing: 454 Life Sciences/Roche Diagnostics
2. Solexa sequencing/Illumina, Genome Analyzers, HiSeq2000, HiSeq2500
MiSeq, and HiSeq X 10, …
3. SOLiD sequencing/Life Technologies: 5500 W Series Genetic Analyzer
4. Helico BioSciences: Single molecule sequencing
5. Pacific Biosciences: Single molecule sequencing. Read length: a few kB
6. Oxford Nanopore technologies: Single molecule sequencing
7. Polonator: open source
8. Ion Torrent/Life Technologies: personal sequencer: non optical sequencing
9. ………
Major sequence databases
Ø  Three almost equivalent biological sequence databases
International Sequence Database Collaboration
1.  GenBank at NCBI
2.  European Molecular Biology Laboratory (EMBL) Nucleotide
Sequence Database at European Bioinformatics Institute (EBI)
3.  DNA database of Japan (DDBI)
Ø  Features
1.  All published biological sequences are traditionally requested to
be deposited in the one of these three databases.
2.  Data are exchanged among these three databases on a daily basis.
3.  However, with the inundation of short read DNA sequences
generated by next-generation sequencing technologies, storing
these sequences is a huge challenge.
Available sequences grow faster than CPU speed
Ø  Even before the genomic era, both the number and length of
sequences increased exponentially with the time.
Ø Clearly, effective analysis of these DNA sequences requires using
computers and effective algorithms.
Ø  Although the number of transistors in a computer also increased
exponentially in the same time period, the number/length of sequences
increases even faster than the number of transistors in a CPU.
rt
ln N (t ) = rt + ln N 0 .
lnN(t)
N (t ) = N 0e ;
(t)
An update of the analysis
https://www.nlm.nih.gov/about/2014CJ.html
An update of the analysis (continued)
https://www.genome.gov/images/content/costpergenome2015_4.jpg
Sequence data explosion
Ø  Since 1995, the number of sequenced genomes has also increased
exponentially.
Complete and Permanent Draft Genome Total in the GOLD Database
Sequence data explosion
Genome Total in the GOLD Database in each year
Sequence data explosion
Project Totals in GOLD (Domain Group)
Sequence data explosion
Ø  Phylogenetic distribution of Bacterial Genome Projects October 2014,
36,959 projects: http://www.genomesonline.org
Sequence data explosion
Ø Major Sequencing Centers October 2014, 56,020 projects: http://
www.genomesonline.org
Sequence data explosion
Ø  Major Sequencing Centers for Archaeal and Bacterial Genomes
October 2014, 37,885 projects: http://www.genomesonline.org
Sequence data explosion
Ø  Major Sequencing Centers for Archaeal and Bacterial Genomes
October 2014, 37,885 projects: http://www.genomesonline.org
Sequence data explosion
Ø Since 2006, the number of meta-genome sequences has increased
exponentially thanks to the advent of NGS technologies.
Ø 8/20/2014, about 467 meta-genomes are sequenced or are in the
process of sequencing.
http://www.genomesonline.org/cgi-bin/GOLD/metagenomic_classification.cgi
https://www.genome.gov/images/content/costpermb2015_4.jpg
Sequence data explosion
Ø The speed of computers also increase exponentially with the time.
Ø However, how can we use the ever powerful computers to solve
biological problems is a very challenging task for both computer
science and biology research communities.
Sequence-related data explosions
Ø  More and more biological research depends on computational
analyses.
Human genome sequence explosion
Ø The initial human genome projects (the public and Celera projects, drafts
finished in 2001) produced consensus sequences from several individuals.
Ø The 1000 Genome Project (http://www.1000genomes.org/) aims to
sequence thousands of carefully selected individual genomes, including
parents and child trios from different human populations.
Ø  The UK 10K Genome Project (http://www.uk10k.org/goals.html): funded
by Welcome Trust to sequence tens of thousands UK individual genomes.
Ø The US 1M Genome Project: funded by the Obama Precision Medicine
Initiative to sequence 1M genomes of Americans (
https://www.whitehouse.gov/precision-medicine)
Ø The GenomeDemark project (http://www.genomedenmark.dk/english/)
will sequence the entire Demark population.
Ø So far, it is estimated that more than 100,000 individual human genomes
have been sequenced, mainly by NGS technologies, majority of them are
not released due to privacy concerns.
Ø  We are soon entering the precision/personalized medicine era, and how to
properly use the data to interpret the variations of phenotypes caused by
the variations in individual genomes is a great challenge.
What is genomics?
Ø  The availability of whole genome sequences of organisms has led to
the birth of Genomics that studies the organisms based on the genetic
information encoded in the genomes.
Ø  According to the subjects of the study, genomics can be divided into:
1. Functional genomics, which is coupled with the development of
relevant high-throughput technologies, such as,
•  Microarray/RNA sequencing(RNA-Seq): transcriptomics
•  Chromosome precipitation followed by microarray hybridization (ChIP-Chip)
or sequencing (ChIP-seq): epigenomics to characterize transcription factor
binding sites and chromatin states or markers.
•  DNAse-seq: to determine the accessibility of chromatin (nucleosome free
region)
•  Chromatin conformation capture (3C) and its derivatives: to determine the
physical proximity of linearly distant DNA segments
•  Mass spectrometry: Proteomics
•  Nucleus magnetic resonance (MR) and mass spectrometry: metabolomics
2. Comparative/evolutionary genomics: studies organisms by
identifying the similarity and difference between their genomes.
What is Bioinformatics?
Ø  For a short answer:
“Bioinformatics is the use of computational methods to study
biological data and problems”.
Ø  For a more detailed answer:
Bioinformatics is
1.  “The development and use of computational methods for studying
the structure, function, and evolution of genes, proteins and
whole genomes;”
2.  “The development and use of methods for the management and
analysis of biological information arising from genomics and
high-throughput experiments.”
Population genetics, molecular evolution and sequence
analysis
Ø According to the evolutionary theory, biological sequences are related
to one another through heredity and variation;
Ø Sequence analysis methods are thus based on the principles of
evolution of sequences.
Ø Therefore, to analyze sequences correctly, we must understand
1. The dynamics changes of genes (loci) in a population of the same
species — population genetics; and
2. How gene sequences change during the course of evolution among
different species — molecular evolution.
Sequence Similarity
Ø The similarity of two sequences can be identified by aligning the
sequences using an alignment method/algorithm, such as the BLAST
or Smith-Waterman method/algorithm.
Ø Two parameters to describe the similarity of two sequences
1. Identity
2. Similarity
Identities = 38/139 (27%), Similarity = 66/139 (47%), Gaps = 9/139
(6.5%)
LELTYIVNFGSELAVVSMLPTFFETTFDLPKATAGILASCFAFVNLVARPAGGLISDSVG
+
Y + FG +A + LPT+ T +
AG
+ FA
++ARP GG +SD +
MSFLYAIVFGGFVAFSNYLPTYITTIYGFSTVDAGARTAGFALAAVLARPVGGWLSDRIA
SRKNTMGFLTAGLGVGYLVMSMIKPGTFTGTTGIAVAVVITMLASFFVQSGEGATFALVP
R
+ L
+ +
P ++ T I +AV + +
G G FA V
PRHVVLASLAGTALLAFAAALQPPPEVWSAATFITLAVCLGV--------GTGGVFAWVA
-LVKRRVTGQVAGLVGAYGNVG
G V G+V A G +G
RRAPAASVGSVTGIVAAAGGLG
Homologous Sequence
Ø Homology: If the two sequences have a high enough similarity, it is
highly likely that they have evolved from a common ancestral
sequence, and we say that they are homologous to each other.
For example, if two sequences of 100 amino acids have 80% of
identity, the probability by chance that the two sequences share this
level of identity is very small (1/20)80.
Ø Homology of two sequences can only be inferred computationally, but
is difficult to be tested experimentally.
Orthologs and Paralogs
Ø There are two distinct types of homologous relationships, which
differ in their evolutionary history and functional implications.
Ø  Orthologs: Evolutional counterparts derived from a single ancestral
gene in the last common ancestor (LCA) of the given two species.
Therefore, orthologous genes are related due to vertical evolution.
Orthologous genes typically have the same function.
Ø  Paralogs: homologous genes evolved through duplication within
the same or ancestral genome. Therefore, paralogous genes are
related due to duplication events. Paralogous genes do not necessary
have the same function.
duplication
speciation
speciation
Divergence evolution
Ø  When the similarity between two sequences is very low, say, 8%
identity, then they could be still homologous due to divergent
evolution;
Speciation or
duplication
homologues
Ø  Divergently evolved genes usually have similar biochemical functions.
Convergence evolution
Ø When the similarity between two sequences are very low, say, 8%,
they could be of different origin, and the observed sequence
similarity is due to convergent evolution under functional selection
during the course of evolution. These two sequences are called
analogues.
analogues
Ø Analogues may have similar biochemical functions, and they usually
only share several amino acids in the active site of enzymes, called
motifs.
Horizontal gene transfer (HGT)
Ø During evolution, a progeny obtains its genes from its ancestor
(vertical gene transfer), however, it also can obtain genes from other
species, genera, or even taxa. This phenomenon is called horizontal
gene transfer (HGT) or lateral gene transfer (LGT).
Ø HGT is very pervasive, in particular, in prokaryote, and is believed to
be a major driving force for evolution.
LCA (Last common ancestor)
• 
Bacteria
Archaea
Eukaryota
Vertical
gene
transfer
Horizontal gene transfer