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![Three case studies
Mining –omics data
Predicting structural aspects of protein residues
Automated alphabet reduction for protein datasets
In all these three case studies we use the same
evolutionary learning system: BioHEL [Bacardit et al.,
09]](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-85-2048.jpg)
![BioHEL BioHEL [Bacardit et al., 09] is an evolutionary
learning system that applies the Iterative Rule
Learning (IRL) approach
Designed explicitly to deal with noisy large-scale
datasets
IRL was first used in EC by the SIA system
[Venturini, 93]](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-86-2048.jpg)
![Functional Network Reconstruction
for seed germination Microarray data obtained from seed tissue of Arabidopsis
Thaliana
122 samples represented by the expression level of
almost 14000 genes
It had been experimentally determined whether each of
the seeds had germinated or not
Can we learn to predict germination/dormancy from the
microarray data?
[Bassel et al., 2011]](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-97-2048.jpg)
![Steps for CM prediction (Nottingham
method)
1. Prediction of
Secondary structure (using PSIPRED)
Solvent Accessibility
Recursive Convex Hull
Coordination Number
2. Integration of all these predictions plus other sources of
information
3. Final CM prediction (using BioHEL)
Using BioHEL [Bacardit et al., 09]](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-115-2048.jpg)
![Target for reduction: the primary sequence
The primary sequence of a protein is
an usual target for such simplification
It is composed of a quite high cardinality
alphabet of 20 symbols, which share
commonalities between them
One example of reduction widely used in
the community is the hydrophobic-polar
(HP) alphabet, reducing these 20 symbols
to just two
HP representation usually is too simple,
too much information is lost in the
reduction process [Stout et al., 06]
Can we automatically generate these
reduced alphabets and tailor them to
the specific problem at hand?](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-128-2048.jpg)
![Automated Alphabet Reduction
[Bacardit et al., 09]
• We will use an automated information theory-driven
method to optimize alphabet reduction policies for PSP
datasets
• An optimization algorithm will cluster the AA alphabet into
a predefined number of new letters
• Fitness function of optimization is based on the Mutual
Information (MI) metric. A metric that quantifies the
interrelationship between two discrete variables
– Aim is to find the reduced representation that maintains as much
relevant information as possible for the feature being predicted
• Afterwards we will feed the reduced dataset into a
learning method to verify if the reduction was proper](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-129-2048.jpg)
![Automated Alphabet Reduction
Our method produces better reduced alphabets than other
reduced alphabets from the literature and than other expert-
designed ones
Alphabets
from the
literature
Expert
designed
alphabets
Alphabet Letters CN acc. SA acc. Diff. Ref.
AA 20 74.0±0.6 70.7±0.4 --- ---
Our method 5 73.3±0.5 70.3±0.4 0.7/0.4 [Bacardit et al., 07]
WW5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Wang & Wang, 99]
SR5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Solis & Rackovsky, 00]
MU4 5 72.6±0.7 69.4±0.4 1.4/1.3 [Murphy et al., 00]
MM5 6 73.1±0.6 69.3±0.3 0.9/1.4 [Melo & Marti-Renom, 06]
HD1 7 72.9±0.6 69.3±0.4 1.1/1.4 [Bacardit et al., 07]
HD2 9 73.0±0.6 69.3±0.4 1.0/1.4 [Bacardit et al., 07]
HD3 11 73.2±0.6 69.9±0.4 0.8/0.8 [Bacardit et al., 07]](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-132-2048.jpg)
![Efficiency gains from the alphabet
reduction
We have extrapolated the reduced alphabet to the much
larger and richer Position-Specific Scoring Matrices (PSSM)
representation
Accuracy difference is still less than 1%
Obtained rule sets are simpler and training process is much
faster
Performance levels are similar to recent works in the literature
[Kinjo et al., 05][Dor and Zhou, 07]
Won the bronze medal of the 2007 Humies awards](https://image.slidesharecdn.com/introduction-170331154845/75/Introduction-133-2048.jpg)
More Related Content
Introduction
- 2. Outline What isBioinformatics? Basic molecular biology Public databases Sequence analysis The scales of bioinformatics Biological data mining
- 4. What is Bioinformatics? Several definitions exist. Michael Liebman proposed a quite elegant definition: “The study of the information content and information flow in biological systems and processes” (Michael Liebman) Information content: genome project Information flow in biological systems: molecular transport Biological systems: cells, organisms, … Biological processes: metabolic networks Bioinformatics is the science of using information to understand aspects of Biology. That is, a discipline where techniques such as applied mathematics, computer science, statistics, artificial intelligence, etc. are integrated to solve biological problems
- 5. Information, information, information As we know there have been major advances in the field of molecular biology These have been coupled with advances in laboratory (post)genomic technology This has led to an explosive growth in the collection of biological information This deluge of information has led to an absolute requirement for 1. Computerized databases to store, organize and index the data 2. For specialized tools to view and analyze the data 3. Specialized tools to infer new knowledge from the data
- 6. Areas of research(taxonomyof the Bioinformatics Journal) Genome Analysis Sequence Analysis Phylogenetics Structural Bioinformatics Gene Expression Genetics and Population Analysis Systems Biology Data and Text Mining Databases Bioimage Informatics
- 8. Life begins withCell A cell is the smallest structural unit of an organism that is capable of sustained independent functioning All cells have some common features What is Life? Can we create it in the lab? Read: The imitation game—a computational chemical approach to recognizing life. Nature Biotechnology, 24:1203-1206, 2006
- 9. 2 types ofcells: Prokaryotes & Eukaryotes
- 10. Example of cellsignaling
- 11. Terminology The genomeis an organism’s complete set of DNA. a bacteria contains about 600,000 DNA base pairs human and mouse genomes have some 3 billion. human genome has 23 distinct chromosomes. Each chromosome contains many genes. Gene basic physical and functional units of heredity. specific sequences of DNA bases that encode instructions on how and when to make proteins. Proteins Make up the cellular structure large, complex molecules made up of smaller subunits called amino acids.
- 12. All Life dependson 3 critical molecules DNAs Hold information on how cell works RNAs Act to transfer short pieces of information to different parts of cell Provide templates to synthesize into protein Proteins Form enzymes that send signals to other cells and regulate gene activity Form body’s major components (e.g. hair, skin, etc.) Are life’s laborers! Computationally, all three can be represented as sequences of a certain 4-letter (DNA/RNA) or 20-letter (Proteins) alphabet
- 13. DNA, RNA, andthe Flow of Information TranslationTranscription Replication Weismann Barrier / Central Dogma of Molecular Biology
- 14. Overview of DNAto RNA to Protein A gene is expressed in two steps 1) Transcription: RNA synthesis 2) Translation: Protein synthesis
- 15. DNA: The Basisof Life Deoxyribonucleic Acid (DNA) Double stranded with complementary strands A-T, C-G DNA is a polymer Sugar-Phosphate-Base Bases held together by H bonding to the opposite strand
- 16. RNA RNA issimilar to DNA chemically. It is usually only a single strand. T(hyamine) is replaced by U(racil) Some forms of RNA can form secondary structures by“pairing up” with itself. This can have impact on its properties dramatically. DNA and RNA can pair with each other.http://www.cgl.ucsf.edu/home/glasfeld/tutorial/trna/trna.giftRNA linear and 3D view:
- 17. RNA, continued Several typesexist, classified by function: hnRNA (heterogeneous nuclear RNA): Eukaryotic mRNA primary transcipts with introns that have not yet been excised (pre-mRNA). mRNA: this is what is usually being referred to when a Bioinformatician says “RNA”. This is used to carry a gene’s message out of the nucleus. tRNA: transfers genetic information from mRNA to an amino acid sequence as to build a protein rRNA: ribosomal RNA. Part of the ribosome which is involved in translation.
- 18. Transcription Transcription ishighly regulated. Most DNA is in a dense form where it cannot be transcribed. To start, transcription requires a promoter, a small specific sequence of DNA to which polymerase can bind (~40 base pairs “upstream” of gene) Finding these promoter regions is only a partially solved problem that is related to motif finding. There can also be repressors and inhibitors acting in various ways to stop transcription. This makes regulation of gene transcription complex to understand.
- 19. Definition of aGene Regulatory regions: up to 50 kb upstream of +1 site Exons: protein coding and untranslated regions (UTR) 1 to 178 exons per gene (mean 8.8) 8 bp to 17 kb per exon (mean 145 bp) Introns: splice acceptor and donor sites, junk DNA average 1 kb – 50 kb per intron Gene size: Largest – 2.4 Mb (Dystrophin). Mean – 27 kb.
- 20.
- 21. Splicing and otherRNA processing In Eukaryotic cells, RNA is processed between transcription and translation. This complicates the relationship between a DNA gene and the protein it codes for. Sometimes alternate RNA processing can lead to an alternate protein (splice variants) as a result. This is true in the immune system.
- 22. Proteins: Crucial molecules forthe functioning of life • Structural Proteins: the organism's basic building blocks, eg. collagen, nails, hair, etc. • Enzymes: biological engines which mediate multitude of biochemical reactions. Usually enzymes are very specific and catalyze only a single type of reaction, but they can play a role in more than one pathway. • Transmembrane proteins: they are the cell’s housekeepers, eg. By regulating cell volume, extraction and concentration of small molecules from the extracellular environment and generation of ionic gradients essential for muscle and nerve cell function (sodium/potasium pump is an example) • Proteins are polypeptide chains, constructed by joining a certain kind of peptides, amino acids, in a linear way • The chain of amino acids, however folds to create very complex 3D structures
- 23. Translation The processof going from RNA to polypeptide. Three base pairs of RNA (called a codon) correspond to one amino acid based on a fixed table. Always starts with Methionine and ends with a stop codon
- 24.
- 25. Protein Structure: Introduction Different amino acids have different properties These properties will affect the protein structure and function Hydrophobicity, for instance, is the main driving force (but not the only one) of the folding process
- 26. Protein Structure: Hierarchicalnature of protein structure MKYNNHDKIRDFIIIEAYMFRFKKKVKPEVDMTIKEFILLTYLFHQQENTL PFKKIVSDLCYKQSDLVQHIKVLVKHSYISKVRSKIDERNTYISISEEQRE KIAERVTLFDQIIKQFNLADQSESQMIPKDSKEFLNLMMYTMYFKNIIKK HLTLSFVEFTILAIITSQNKNIVLLKDLIETIHHKYPQTVRALNNLKKQGYL IKERSTEDERKILIHMDDAQQDHAEQLLAQVNQLLADKDHLHLVFE Primary Structure = Sequence of amino acids Secondary Structure Tertiary Local Interactions Global Interactions
- 27. Protein Structure: Whyis structure important? The function of a protein depends greatly on its structure The structure that a protein adopts is vital to it’s chemistry Its structure determines which of its amino acids are exposed to carry out the protein’s function Its structure also determines what substrates it can react with
- 28. Protein Structure: Mostlylacking information Therefore, it is clear that knowing the structure of a protein is crucial for many tasks However, we only know the structure for a very small fraction of all the proteins that we are aware of The UniProtKB/TrEMBL archive contains 23165610 (16886838) sequences The PDB archive of protein structure contains only 84223(76669) structures In the native state, proteins fold on its own as soon as they are generated, amino-acid by amino-acid (with few exceptions e.g. chaperones) can we predict this process as to close the gap between protein sequences and their 3D structures?
- 29. Central Dogma ofBiology: A Bioinformatics Perspective The information for making proteins is stored in DNA. There is a process (transcription and translation) by which DNA is converted to protein. By understanding this process and how it is regulated we can make predictions and models of cells. Sequence analysis Gene Finding Protein Sequence/Stru cture Analysis Assembly Computational Problems
- 31. Information flow inbioinformatics Data enters the “bioinformatics scope” when a scientist deposits an experimental result in an appropriate archive The archive curates and annotates the data The data is released to the public Afterwards, the data may be retrieved/analysed: Integrating the new entry into a search engine Extracting useful subsets of the data Deriving new types of information from the data Aggregating the data, by homology, function, structure Reannotating the data with new discovered/inferred info. Quality of data depends on many factors, the techniques used to experimentally create the data, degree of inference and prediction involved in the annotation process, etc. Many publicly available databases: http://en.wikipedia.org/wiki/List_of_biological_databases
- 32. NCBI’s Entrez system http://www.ncbi.nlm.nih.gov/ Entrezis a search and retrieval system that integrates information from databases at NCBI (National Center for Biotechnology Information).
- 33. Uniprot http://www.uniprot.org TheUniversal Protein Resource (UniProt) is a collaboration between the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics and the Protein Information Resource (PIR)
- 34. KEGG - http://www.genome.jp/kegg/ Not just about genes/proteins but also pathways, that is, their interactions
- 35.
- 37. Sequences Be itDNA, RNA or proteins we have many data that can be represented as sequences of a certain alphabet Many generic algorithms to deal with biological sequences exist Sequence alignment Motif representation
- 38. Sequence Alignment Isthe assignment of residue-residue correspondences between nucleotide/proteomic sequences Query 1 MQNSHSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRY 60 MQNSHSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRY Sbjct 1 MQNSHSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRY 60 Query 61 YETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSV 120 YETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLL Sbjct 61 YETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLL------------- 107 ... Query 301 QPTTPVSSFTSGSMLGRTDTALTNTYSALPPMPSFTMANNLPMQPPVPSQTSSYSCMLPT 360 QPTTPVSSFTSGSMLGRTDTALTNTYSALPPMPSFTMANNLPMQ + C P+ Sbjct 281 QPTTPVSSFTSGSMLGRTDTALTNTYSALPPMPSFTMANNLPMQ-----DSFHLECQFPS 335 Query 361 S-PSVN 365 P VN Sbjct 336 KFPGVN 341 gap matches mismatches
- 39. Motivation Similarity isexpected among biomolecules that are descended from a common ancestor. Mutations cause differences, but survival of the organism requires that mutations occur in regions that are less critical to function Important catalytic, regulatory or structural regions remain similar An alignment between two or more genetic or proteomic sequences represents an explicit hypothesis via their evolutionary histories. Thus comparison of related gene/protein sequences have been instrumental in shedding light into the information content of these sequences and their biological functions.
- 40. Definition and aims Why align sequences? 1. Start with a query sequence with unknown properties and search within a database of millions of sequences to find those which share similarity with the query. 2. Start with a small set of sequences and identify similarities and differences among them. 3. In many sequences or very long sequences, detect commonly occurring patterns
- 41. Similarity vs. Homology Similarity is the observation or measurement of resemblance and difference, independent of the source of resemblance. There are many examples of different organisms with functionally similar organs that came from distinc evolutionary origins When similarity is due to a common ancestry, we call it homology. Sequence alignment helps inferring homology hypothesis: If two sequences are very similar, it is probable that there is a common origin Therefore, if we know some information (structure, function) from sequence X, and sequence X is similar to sequence Y, it is probable that the same information applies to Y
- 42. Metrics of similarity:Definitions Gap: a break in the alignment, in either one of the sequences. For nucleotides, a consequence of an insertion or deletion mutation. For proteins, it’s more difficult to say. Regions of matching residues. Indicate parts of a sequence that are well conserved Mismatched residues. For nucleotides, a consequence of a substitution mutation Less conserved regions
- 43. Metrics of similarity:Distance scoring Distance scoring Given an alignment with matches, mismatches and gaps, we compute a score following: For each mismatch, score is increased by 2 For each gap, score is increased by 4 For each match, no increase in score Higher score, less similarity Equivalent metrics exist for similarity (not distance) where higher score means good similarity = 18 A – G C C G T A T A C G A - - T - T 0 4 0 2 4 4 0 4 0
- 44. Metrics of similarity:Mismatches and gaps Are all mismatches equally bad? For protein sequences, there are several subgroups of amino acids with similar properties. Mismatches within a group have less impact For nucleotide sequences, transition mutations (a↔g and t↔c) are more common than transversions (a or g ↔ t or c) mutations Distance scoring of mismatches could be smarter substitution matrices Using statisical analysis on large corpus of real sequences to generate better scores How to penalize gaps Each gap slot gets equal distance score One score to open a gap, another (smaller) score to extend the same gap
- 45. Global vs Localalignment We know how to score good or bad alignments How to find the optimal one? Two classes of alignment methods Global alignment Finds the best alignment of one entire sequence with another entire sequence Local alignment Find the best alignment of one segment of a sequence against another segment of another sequence
- 46. Exact vs. Approximatemethods Exact methods for both global and local alignment exist, based on dynamic programming, but are slow Good enough when there are few sequences Not so good when comparing a target sequence to a database of millions of known sequences Approximate methods have been used for many years for large- scale alignment tasks They use some kind of heuristic to speed up the alignment process BLAST (Basic Local Alignment Search Tool) is the most famous approximate method It identifies potential hits by looking for perfect matches of very small sub-sequences (seeds) It only tries to create a full alignment for sequences where several seeds are identified PSI-BLAST: version that takes into account that multiple hits are identified. It constructs a tailored substitution matrix based on hits and then refines the alignment
- 47. Multiple Sequence Alignment When we have to align more than two sequences Progressive methods (e.g. ClustalW) Start with seed alignment Iteratively incorporate other alignments to seed, without modifying what is aligned so far ClustalW uses phylogenetic trees (representations of the evolutionary relationship between sequences) to progressively construct MSA Iterative methods (e.g. MUSCLE) Can re-edit the partial MSA based on the newly incorporated alignments
- 48.
- 49. Motifs When visualisinga MSA we can see regions of high agreement and regions of low agreement. The high agreement regions define that a certain protein belongs to a family What if we concentrate on modelling and identifying these regions instead of the whole sequences Motif finding
- 50. DNA -> RNA-> Proteins
- 51. DNA Coding/non coding SNPs Copy number variation Assembly Methylation Primer design
- 52. Coding/Non Coding Identifyingthe regions from an organism’s genome that contain genes Many different factors involved in this identification Promoter identification Long enough Open Reading Frames (ORF) Splice variants Introns/Exons (in Eucaryotes) Statistical properties of gene-coding DNA HMM are also used for gene finding
- 53. Single Nucleotide Polymorphisms (SNPs) One base-pair variation in DNA In most cases in non-coding regions of DNA, but not always When frequent enough in a population they can be linked to specific traits, e.g. a disease SNP microarrays can be used to probe hundreds of thousands of SNPs in parallel In reality few SNPs act on their own Genome-Wide Association Studies identify groups of SNPs linked to a certain condition
- 54. Copy Number Variation In general two copies of each gene exist in a genome It may be the cases than more/less than two copies exist of a certain gene for a specific sub-population It has been suggested that certain CNV can be linked to specific diseases
- 55. Genome assembly Sequencingtechnologies are able to read (sequence) a complete genome as a series of short overlapping fragments How to assemble back all these fragments? Greedy approach Pair-wise alignments of all fragments Merge fragments of largest overlap Keep iterating until all segments are merged Worked more or less well on old sequencing technologies, not so well on next-generation sequencing data, due to smaller fragment sizes and larger error rate
- 56. Genome mapping Givena large set of short fragments, as a result of next- generation sequencing, map them to a reference genome Different from previous one. We do not want to reconstitute a complete genome, just identify to which genes each fragment belongs (among other applications). Speed is an issue Modern methods (e.g. SOAP2) compress the genome and are able to align the fragments in the compressed space
- 57. Methylation It isa chemical reaction that can block a certain region of a chromosome, preventing its transcription The process can be reverted, so essentially it is an on/off switch of the affected gene Specialised microarrays exist for the high-throughput detection of methylated genes Afterwards, data analysis can take place
- 58. DNA library specification •A DNA library is a combinatorial set of DNA sequences suited to manufacture via DNA reuse • The first stage towards the creation of a DNA library is the formal specification of the target DNA molecules that comprise it • A set of sequences does not convey the intention behind the library Key challenge is to enable precise editing of DNA sequences in an extensible and reproducible manner whilst avoiding manual handling of these unwieldy objects
- 59. DNALD library format A DNALD library consists of three sets of definitions: inputs, intermediates and outputs, with different semantics Inputs: existing DNA sequences to be provided with design Intermediates: conceptual means of factoring commons seqs Outputs: to be produced through DNA reuse
- 60. DNALD expressions ADNALD expression is a combination of explicit sequences, definition names, operators and functions that are interpreted according to rules of precedence and association ("evaluated") to produce a set of DNA sequences. Definitions bind names to the results of expressions.
- 61. Workbench interface text editorwith: • syntax highlighting • auto-completion • code folding • etc. manage projects viewed from different perspectives
- 62. CADMAD’s DNALD (DNALibrary Design) A specification language that produces a set of target DNA sequences as a function of operations on a set of inputs To maximise CADMAD's impact the specification process must be: user friendly and debuggable but expressively powerful enough to: define non-trivial combinatorial constructs communicate degrees of freedom >Ret_human GGCCTCTACTTCTCGAGGGATGCTTACTGGGAGAAGCTGTATGTGGACCAGGCGGCCGGCA CGCCCTTGCTGTACGTCCATGCCCTGCGGGACGCCCCTGAGGAGGTGCCCAGCTTCCGCCT GGGCCAGCATCTCTACGGCACGTACCGCACACGGCTGCATGAGAACAACTGGATCTGCATC CAGGAGGACACCGGCCTCCTCTACCTTAACCGGAGCCTGGACCATAGCTCCTGGGAGAAGC TCAGTGTCCGCAACCGCGGCTTTCCCCTGCTCACCGTCTACCTCAAGGTCTTCCTGTCACC CACATCCCTTCGTGAGGGCGAGTGCCAGTGGCCAGGCTGTGCCCGCGTATACTTCTCCTTC TTCAACACCTCCTTTCCAGCCTGCAGCTCCCTCAAGCCCCGGGAGCTCTGCTTCCCAGAGA CAAGGCCCTCCTTCCGCATTCGGGAGAACCGACCCCCAGGCACCTTCCACCAGTTCCGCCT GCTGCCTGTGCAGTTCTTGTGCCCCAACATCAGCGTGGCCTACAGGCTCCTGGAGGGTGAG GGTCTGCCCTTCCGCTGCGCCCCGGACAGCCTGGAGGTGAGCACGCGCTGGGCCCTGGACC GCGAGCAGCGGGAGAAGTACGAGCTGGTGGCCGTGTGCACCGTGCACGCCGGCGCGCGCGA GGAGGTGGTGATGGTGCCCTTCCCGGTGACCGTGTACGACGAGGACGACTCGGCGCCCACC TTCCCCGCGGGCGTCGACACCGCCAGCGCCGTGGTGGAGTTC>Ret_mouse GGCCTCTATTTCTCAAGGGATGCTTACTGGGAGAGGCTGTATGTAGACCAGCCAGCTGGCA CACCTCTGCTCTATGTCCATGCCCTACGGGATGCCCCTGGAGAAGTGCCGAGCTTCCGCCT GGGCCAGCATCTCTATGGCGTCTACCGTACACGGCTGCATGAGAATGACTGGATCCGCATC AATGAGACTACTGGCCTTCTCTACCTCAATCAGAGCCTGGACCACAGTTCCTGGGAACAGC TCAGCATCCGCAATGGTGGTTTCCCCCTGCTCACCATCTTCCTCCAGGTCTTTCTGGTGGA AAACTGCCAGGAGTTCAGCGGTGTCTCCATCCAGTACAAGCTGCAGCCTTCCAGCATCAAC TGCACTGCCCTAGGTGTGGTCACCTCACCCGAGGACACCTCGGGGACCCTATTTGTAAATG ACACAGAGGCCCTGCGGCGACCTGAGTGCACCAAGCTTCAGTACACGGTGGTAGCCACTGA CCGGCAGACCCGCAGACAGACCCAGGCTTCGCTAGTGGTCACTGTGGAGGGGACATCCATT ACTGAAGAAGTAGGCT >Ret_zebrafish GGGCTGTATTTTCCTCAAAGGCTTTACACAGAGAACATCTACGTGGGTCAGCAGCAGGGAT CACCGTTGCTTCAGGTCATTTCAATGCGGGAATTCCCTACAGAGAGGCCTTATTTCTTCCT GTGCTCGCACAGAGACGCTTTTACATCATGGTTTCACATAGATGAGGCGTCCGGAGTTCTT TATCTCAACAAAACCCTGGAGTGGAGCGACTTCAGTAGTTTACGCAGCGGCTCAGTTCGCT CCCCGAAGGATCTCTGACCTATCAGTTAGAGATTGTCGACAGGAACATCACTGCTGAAGCT CAGTCCTGTTACTGGGCGGTTAGTCTTGCACAAAACCCGAATGATAATACAGGCGTTCTCT ATGTGAACGACACCAAAGTGTTACGCAGACCAGAGTGCCAAGAGCTGGAGTATGTGGTCAT TGCCCAGGAGCAGCAGAACAAGCTTCAGGCCAAGACACAGCTCACCGTCAGTTTTCAAGGC GAAGCAGATTCACTGAAAACGGATG >Ret_chicken GGTCTGTACTTCCCCAGAAAGGAGTACTCAGAGAACGTCTACATTGACCAGCCAGCAGGTG CGCCGCTCCTACGCATCCACGCCTTGAGGGATTCACATGGGAAACAGCCCACTTTCATCTG TGCCAGAAGTCTCATCATTTCTCGAGCAAGATCCCATGAAAATCACTGGTTTCAAATCAGA GAAAAAATGGGACTTCTCTACCTCAGCAAGAGCCTAGATAGAGAAGACTTTAACATGCTGT CTGTAGGAAACTGGATGCCATTATCAAAGGTGATGCTGTATGTCTTCCTCTCATCTCACCC TTTCCAAGAGAAGGAATGTGACTCTGCTACTCGTACCACAGTCGTCCTCTCTTTGATCAAT GCTACTGCACCAGCTTGCAGTTCACTGTCAGCAAGGCAGCTTTGCTTCACAGAAATGGATC TCTCCTTTCACATCAAGGAGAATAAACCCCCTGGTACATTTCATCAGCTCCAGTTACCCTC AGTTCATCATCTGTGTCAGAATCTCAGCATTACCTACAAACTGTTGGCAGCCGAAGGCCTG CCTTTTCGGTACAATGAGAACACCACTGGTGTGAGTGTAACACAGCGCCTAGATCGAGAGG AGAGAGAGAGATATGAGCTGATCGCCAAATGCACCGTGAGAGAAGGCTTCAGGGAAATGGA GGTTGAGGTGCCCTTCCTCGTCAACGTGTTAGATGAAGATGACTCTCCTCCCTTCCTTCCC
- 63. RNA Expression Structureprediction
- 64. RNA expression Notall genes are transcribed/translated into proteins all the time The expression of genes is highly sophisticated and depends on many factors Identifying the genes being expressed in a given point of time in a specific tissue provides crucial information about the roles and interactions of such genes Compare the genes expressed between different groups of samples to identify those that are differentially expressed Identify co-expressed genes, that present patterns of correlation
- 65. Measuring RNA expression RT-PCR (Real-time reverse polimerase chain reaction) Measures accurately the expression of a pre-determined gene RNA Microarrays Measures, in parallel, the expression of tens of thousands of genes RNA-Seq The next-generation sequencing variant for measuring gene expresison
- 66. RNA Structure prediction A RNA sequence can bind with itself to create complex shapes with a certain pattern of loops Can we predict, from a given sequence, the structural shape of the RNA?
- 67. Proteins Protein classification Structure prediction Structure comparison Function and interaction
- 68. Protein classification Proteinscan be annotated in many different ways Function DNA-binding? Enzyme? Tissue/Cellular/Sub-cellular localisation Interacting with other proteins? Can we predict this annotation using ML? We need to transform the protein sequence into a uniform representation of equal size for all proteins Many different representations exist Several of these problems can be modelled as a hierarchical classification problem
- 69. Protein Structure PredictionPSP aims to predict the 3D structure of a protein based on its primary sequence
- 70. Protein Structure Prediction PSP is an open problem. The 3D structure depends on many variables It has been one of the main holy grails of computational biology for many decades Impact of having better protein structure models are countless Genetic therapy Synthesis of drugs for incurable diseases Improved crops Environmental remediation
- 71. Prediction types ofPSP There are several kinds of prediction problems within the scope of PSP The main one, of course, is to predict the 3D coordinates of all atoms of a protein (or at least the backbone) based on its primary sequence There are many structural properties of individual residues within a protein that can be predicted, for instance: The secondary structure state of the residue If a residue is buried in the core of the protein or exposed in the surface Accurate predictions of these sub-problems can simplify the general 3D PSP problem
- 72. 3D Protein StructurePrediction Some PSP methods try to find similar proteins and then adapt the structure of the homolog (template) to the target protein Homology Modeling Other methods try to find the structure of the protein from scratch (Ab Initio Modelling), optimizing some energy function that models the stability of the protein, in case no homolog can be identified In between there are other kind of methods, for varying degrees of good homology of our target, for instance, Fold Recognition or Threading • These methods identify a target based on more than homology (i.e. sequence alignment).
- 73. Coordination Number PredictionTworesidues of a chain are said to be in contact if their distance is less than a certain threshold (e.g. 8Å) CN of a residue : count of contacts that a certain residue has CN gives us a simplified profile of the density of packing of the protein ContactPrimary Sequence Native State
- 74. Contact Map predictionPrediction, given two residues from a chain, whether these two residues are in contact or not This problem can be represented by a binary matrix. 1= contact, 0 = non contact Plotting this matrix reveals many characteristics from the protein structure Very sparse characteristic: Less than 2% of contacts in native structures helices sheets
- 75. Other predictions Otherkinds of residue structural aspects that can be predicted Solvent accessibility: Amount of surface of each residue that is exposed to solvent Recursive Convex Hull: A metric that models a protein as an onion, and assigns each residue to a layer. Formally, each layer is a convex hull of points These features (and others) are predicted in a similar was as done for SS
- 76.
- 77. Protein Structure Comparison Protein Structure Comparison (PSC) aims at Assess the degree of similarity between protein structures Given a query structure, identify other proteins with similar structure Why? Group proteins by structural similarities Determine the impact of individual residues on the protein structure Identify distant homologues of protein families Predict function of proteins with low degree of primary structure (i.e.. sequence) similarity with other proteins Engineer new proteins for specific functions Assess ab-initio predictions
- 78. Protein Structure Comparison Sequence-Structure-Function relationships 1) Conserved 1º sequences similar structures 2) Similar structures conserved 1º sequences 3) Similar structures conserved function PSC shares many similarities with sequence alignment. Our aim is to infer new knowledge from the comparison process ?
- 79. Protein Structure ComparisonExisting Approaches SSAP (Orengo & Taylor, 96) ProSup (Feng & Sippl, 96) DALI (Holm & Sander, 93) CE (Shindyalov & Bourne, 98) LGA (Zemla, 2003) SCOP (Murzin, Brenner, Hubbard & Chothia, 95) CATH (Orengo, Mithie, Jones, Jones, Swindells & Thornton, 97) ProCKSI – Consensus of multiple PSC methods
- 80. Prediction of ProteinFunction In an ideal world, the cascade of inference should flow from sequence structure function That is, if we can identify similar sequences of structures to our query target we can (at varying degrees of certainty) infer that they have similar function
- 81. Prediction of ProteinFunction As proteins evolve, they may Retain function and specificity Retain function but alter specificity Change to a related function, or a similar function in a different metabolic contxt Change to a completely unrelated function How much must a protein change before the function changes? Sometimes, not at all. There are many cases of proteins with different functions in different environments
- 82. Prediction of ProteinFunction Thus, sequence or structure similarity is not always reliable to assign function Other ways of determining protein function By identifying patterns of co-regulated genes Using data from Microarray experiments By identifying protein-protein interactions
- 83. Prediction of ProteinFunction A related question is: where is the function of a protein taking place? active site Several methods exist to predict active/binding sites of proteins from local patterns of sequence or structure A raw way of doing this prediction is to take a look at the conserved residues of a sequence they may be related to either the core of the protein (structural stability) or the function of a protein (a change of function is a risk for survival) More sophisticated methods exists to learn how to predict active sites. They use ML, in a similar way used to predict residue structural features in PSP Still, it is a very tough problem, and ML methods are not much better than blast-based methods
- 85. Three case studies Mining –omics data Predicting structural aspects of protein residues Automated alphabet reduction for protein datasets In all these three case studies we use the same evolutionary learning system: BioHEL [Bacardit et al., 09]
- 86. BioHEL BioHEL [Bacarditet al., 09] is an evolutionary learning system that applies the Iterative Rule Learning (IRL) approach Designed explicitly to deal with noisy large-scale datasets IRL was first used in EC by the SIA system [Venturini, 93]
- 87. BioHEL’s learning paradigmIRL has been used for many years in the ML community, with the name of separate-and-conquer
- 88. BioHEL’s objective functionAn objective function based on the Minimum- Description-Length (MDL) (Rissanen,1978) principle that tries to promote rules with High accuracy: not making mistakes High coverage: covering as much examples as possible without sacrificing accuracy. Recall (TP/(TP+FN)) will be used to define coverage Low complexity: rules as simple and general as possible The objective function is a linear combination of the three objectives above
- 89. BioHEL’s objective function Intuitively, we would like to have accurate rules covering as much examples as possible. However, in complex and inconsistent domains it is rare to obtain such rules In these cases, easier path for evolutionary search is to maximize accuracy at the expense of coverage Therefore, we need to enforce that the evolved rules cover enough examples
- 90. BioHEL’s objective function Three parameters define the shape of the function The choice of the coverage break is crucial for the proper performance of the system Also, coverage term penalizes rules that do not cover a minimum percentage of examples or that cover too many
- 91. BioHEL’s characteristics Attributelist rule representation Automatically identifying the relevant attributes for a given rule and discarding all the other ones The ILAS windowing scheme Efficiency enhancement method, not all training points are used for each fitness computation An explicit default rule mechanism Generating more compact rule sets Iterative process terminates when it is impossible to evolve a rule where the associated class is the majority class among the matched examples At this point, all remaining training instances are assigned to the default class
- 93. Mining –omics data Biological data can be generated at many different levels Genomics (DNA) Transcriptomics (RNA) Proteomics (proteins) Metabolomics (small compounds) Lipidomics (lipids) Hundreds of –omics have been catalogued
- 94. How an –omicsdataset looks like? In most cases datasets present a similar structure Each sample is characteristed by a large number of variables (RNA, Proteins, lipids, etc.) Each variable indicates (usually quantitatively) the presence of that element in the sample Due to the high cost of most –omics technologies, variables >> samples Problems of over-fitting
- 95. What can wedo with the dataset? In most cases, samples are annotated with a qualitative label Cancer/Non-cancer patients Samples of seed tissue for which it is known if the seed germinated or not Age of the sample Therefore, we can treat these datasets as classification problems, and generate prediction models from the data Not just as classification problems Clustering/Biclustering Association Rule Mining Regression
- 96. But in mostcases, domain experts are not (only) interested in predictions Biomarker identification Identify the key variables Most strongly associated to each outcome Using e.g. t-tests to identify those Presenting higher prediction capacity As identified by ML methods Identify interactions between variables By presenting very high (anti)correlation between them By acting together to generate predictions
- 97. Functional Network Reconstruction forseed germination Microarray data obtained from seed tissue of Arabidopsis Thaliana 122 samples represented by the expression level of almost 14000 genes It had been experimentally determined whether each of the seeds had germinated or not Can we learn to predict germination/dormancy from the microarray data? [Bassel et al., 2011]
- 98. Generating rule sets BioHEL was able to predict the outcome of the samples with 93.5% accuracy (10 x 10-fold cross- validation Learning from a scrambled dataset (labels randomly assigned to samples) produced ~50% accuracy If At1g27595>100.87 and At3g49000>68.13 and At2g40475>55.96 Predict germination If At4g34710>349.67 and At4g37760>150.75 and At1g30135>17.66 Predict germination If At3g03050>37.90 and At2g20630>96.01 and At3g02885>9.66 Predict germination If At5g54910>45.03 and At4g18975>16.74 and At3g28910>52.76 and At1g48320>56.80 Predict germination Everything else Predict dormancy
- 99. Identifying regulators Rulebuilding process is stochastic Generates different rule sets each time the system is run But if we run the system many times, we can see some patterns in the rule sets Genes appearing quite more frequent than the rest Some associated to dormancy Some associated to germination
- 100. Known regulators appearwith high frequency in the rules
- 101. Generating co-prediction networksof interactions • For each of the rules shown before to be true, all of the conditions in it need to be true at the same time – Each rule is expressing an interaction between certain gens • From a high number of rule sets we can identify pairs of genes that co-occur with high frequency and generate functional networks • The network shows different topology when compared to other type of network construction methods (e.g. by gene co- expression) • Different regions in the network contain the germination and dormancy genes
- 102. Experimental validation Wehave experimentally verified this analysis By ordering and planting knockouts for the highly ranked genes We have been able to identify four new regulators of germination, with different phenotype from the wild type
- 104. Prediction of structuralaspects of protein residues Many of these features are due to local interactions of an amino acid and its immediate neighbours Can it be predicted using information from the closest neighbours in the chain? In this simplified example to predict the SS state of residue i we would use information from residues i-1 i and i+1. That is a window of ±1 residues around the target Ri SSi Ri+1 SSi+1 Ri-1 SSi-1 Ri+2 SSi+2 Ri-2 SSi-2 Ri+3 SSi+3 Ri+4 SSi+4 Ri-3 SSi-3 Ri-4 SSi-4 Ri-5 SSi-5 Ri+5 SSi+5 Ri-1 Ri Ri+1 SSi Ri Ri+1 Ri+2 SSi+1 Ri+1 Ri+2 Ri+3 SSi+2
- 105. ARFF file fora simple PSP dataset @relation AA+CN_Q2 @attribute AA_-4 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_-3 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_-2 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_-1 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y} @attribute AA_1 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_2 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_3 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute AA_4 {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,X,Y} @attribute class {0,1} @data X,X,X,X,A,E,I,K,H,0 X,X,X,A,E,I,K,H,Y,0 X,X,A,E,I,K,H,Y,Q,0 X,A,E,I,K,H,Y,Q,F,0 A,E,I,K,H,Y,Q,F,N,0 E,I,K,H,Y,Q,F,N,V,0 I,K,H,Y,Q,F,N,V,V,0 K,H,Y,Q,F,N,V,V,M,1 H,Y,Q,F,N,V,V,M,T,0 Y,Q,F,N,V,V,M,T,C,1
- 106. What information dowe include for each residue? Early prediction methods used just the primary sequence the AA types of the residues in the window However the primary sequence has limited amount of information It does not contain any evolutionary information it does not say which residues are conserved and which are not Where can we obtain this information? Position-Specific Scoring Matrices which is a product of a Multiple Sequence Alignment
- 107. Position-Specific Scoring Matrices(PSSM) – For each residue in the query sequence compute the distribution of amino acids of the corresponding residues in all aligned sequences (discarding those too similar to the query) – This distributions will tell us which mutations are likely and which mutations are less likely for each residue in the query sequence – In essence it’s similar to a substitution matrix but tailored for the sequence that we are aligning – A PSSM profile will also tell us which residues are more conserved and which residues are more subject to insertions or deletions
- 108. PSSM for the10 first residues of 1n7lAA R N D C Q E G H I L K M F P S T W Y V A: 4 -1 -2 -2 0 -1 -1 0 -2 -1 -2 -1 -1 -2 -1 1 0 -3 -2 0 M:-1 -2 -3 -4 -2 -1 -2 -3 -2 1 2 -2 7 0 -3 -2 -1 -2 -1 1 E:-1 0 0 2 -4 2 6 -2 0 -4 -3 1 -2 -4 -1 0 -1 -3 -2 -3 K:-1 2 0 -1 -4 1 1 -2 -1 -3 -3 5 -2 -4 -1 0 -1 -3 -2 -3 V: 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 5 Q:-1 1 0 0 -3 6 2 -2 0 -3 -3 1 -1 -4 -2 0 -1 -2 -2 -3 Y:-2 -1 -1 -3 -3 -1 -1 -3 6 -2 -2 -2 -1 2 -3 -2 -2 1 7 -2 L:-2 -3 -4 -4 -2 -3 -3 -4 -3 2 5 -3 2 0 -3 -3 -1 -2 -1 1 T: 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 2 5 -3 -2 0 R:-2 6 -1 -2 -4 1 0 -3 0 -3 -3 2 -2 -3 -2 -1 -1 -3 -2 -3
- 109. Secondary Structure Prediction –The most usual way is to predict whether a residue belongs to an α helix a β sheet or is in coil state – Several programs can determine the actual SS state of a protein from a PDB file. The most common of them is DSSP – Typically, a window of ±7 amino acids (15 in total) is used. This means 300 attributes (when using PSSM). – A dataset with 1000 proteins with ~250AA/protein would have ~250000 instances
- 110. Secondary Structure Prediction R1R2 R3 Rn-1 Rn Primary sequence MSA PSSM1 PSSM2 PSSM3 PSSMn-1 PSSMn PSSM profile of sequence Windows generation PSSMi-1 PSSMi PSSMi+1 Prediction method SSi? Window of PSSM profilesPrediction
- 111. Other prediction problems This same structure of prediction can be applied to most 1D structural aspects However, many of these features are natively continuous measures (or integer) To treat these problems as classification problems, we need to discretise the output Unsupervised methods are applied Uniform length and uniform frequency disc. UL UF
- 112. PSP datasets aregood ML benchmarks These problems can be modelled in may ways: Regression or classification problems Low/high number of classes Balanced/unbalanced classes Adjustable number of attributes Ideal benchmarks !! http://icos.cs.nott.ac.uk/datasets/psp_benchmark.ht ml
- 113. Contact Map Prediction We participated in the CASP9 competition CASP = Critical Assessment of Techniques for Protein Structure Prediction. Biannual competition Every day, for about three months, the organizers release some protein sequences for which nobody knows the structure (129 sequences were released in CASP9, in 2010) Each prediction group is given three weeks to return their predictions If the machinery is not well oiled, it is not feasible to participate !! For CM, prediction groups have to return a list of predicted contacts (they are not interested in non-contacts) and, for each predicted pair of contacting residues, a confidence level
- 114. Contact Map predictionPrediction given two residues from a chain whether these two residues are in contact or not This problem can be represented by a binary matrix. 1= contact 0 = non contact Plotting this matrix reveals many characteristics from the protein structure helices sheets
- 115. Steps for CMprediction (Nottingham method) 1. Prediction of Secondary structure (using PSIPRED) Solvent Accessibility Recursive Convex Hull Coordination Number 2. Integration of all these predictions plus other sources of information 3. Final CM prediction (using BioHEL) Using BioHEL [Bacardit et al., 09]
- 116. Prediction of RCH,SA and CN We selected a set of 3262 protein chains from PDB- REPRDB with: A resolution less than 2Å Less than 30% sequence identify Without chain breaks nor non-standard residues 90% of this set was used for training (~490000 residues) 10% for test
- 117. Prediction of RCH,SA and CN All three features were predicted based on a window of ±4 residues around the target Evolutionary information (as a Position-Specific Scoring Matrix) is the basis of this local information Each residue is characterised by a vector of 180 values The domain for all three features was partitioned into 5 states
- 118. Characterisation of thecontact map problem Three types of input information were used 1. Detailed information of three different windows of residues centered around The two target residues (2x) The middle point between them 2. Information about the connecting segment between the two target residues and 3. Global protein information. 1 2 3
- 119. Contact Map dataset From the original set of 3262 proteins we kept all that had <250 AA and a randomly selected 20% of larger proteins Still, the resulting training set contained 32 million pairs of AA and 631 attributes Less than 2% of those are actual contacts +60GB of disk space
- 120. Samples and ensembles 50 samples of 660K examples are generated from the training set with a ratio of 2:1 non-contacts/contacts BioHEL is run 25 times for each sample Prediction is done by a consensus of 1250 rule sets Confidence of prediction is computed based on the votes distribution in the ensemble. Whole training process took about 25K CPU hours Training set x50 x25 Consensus Predictions Samples Rule sets
- 121. Contact Map predictionin CASP Predictor groups are asked to submit a list of predicted contacts and a confidence level for each prediction The assessors then rank the predictions for each protein and take a look at the top L/x ones, where L is the length of the protein and x={5,10} From these L/x top ranked contacts two measures are computed Accuracy: TP/(TP+FP) Xd: difference between the distribution of predicted distance and a random distribution
- 122. CASP9 results These twogroups derived contact predictions from 3D models http://www.predictioncenter.org/casp9/doc/presentations/CASP9_RR.pdf
- 123. Understanding the rulesets Each rule set has in average 135 rules We have a total of 168470 rules Impossible to read all of them individually, but we can extract useful statistics For instance, how often was each attribute used in the rules? Full analysis
- 124. Distribution of frequencyof use of attributes All 631 attributes are actually used (min frequency=429) However, some of them are used much more frequently than others
- 125. Top 10 attributesAttributeFrequency Count s PredSS_r1_1 1.48% 18141 PredCN_r1 1.66% 20336 propensity 1.74% 21288 PredSS_r2 1.75% 21350 PredSS_r1 1.82% 22205 PredRCH_r2 1.87% 22856 PredRCH_r1 2.04% 24961 PredSA_r2 2.12% 25891 PredSA_r1 2.39% 29246 separation 4.17% 50951 The four kind of residue’s predictions are highly ranked
- 127. Motivation PSP isa very costly process As an example, one of the best PSP methods CASP8, Rosetta@Home could dedicate up to 104 computing years to predict a single protein’s 3D structure One of the possible ways to alleviate this computational cost is to simplify the representation used to model the proteins
- 128. Target for reduction:the primary sequence The primary sequence of a protein is an usual target for such simplification It is composed of a quite high cardinality alphabet of 20 symbols, which share commonalities between them One example of reduction widely used in the community is the hydrophobic-polar (HP) alphabet, reducing these 20 symbols to just two HP representation usually is too simple, too much information is lost in the reduction process [Stout et al., 06] Can we automatically generate these reduced alphabets and tailor them to the specific problem at hand?
- 129. Automated Alphabet Reduction [Bacarditet al., 09] • We will use an automated information theory-driven method to optimize alphabet reduction policies for PSP datasets • An optimization algorithm will cluster the AA alphabet into a predefined number of new letters • Fitness function of optimization is based on the Mutual Information (MI) metric. A metric that quantifies the interrelationship between two discrete variables – Aim is to find the reduced representation that maintains as much relevant information as possible for the feature being predicted • Afterwards we will feed the reduced dataset into a learning method to verify if the reduction was proper
- 130. Alphabet Reduction protocol 130 Dataset Card=20 ECGA Mutual Information Size= N Dataset Card=N BioHEL Test set Accuracy Ensemble of rule sets
- 131. Automated Alphabet Reduction Competent 5-letter alphabet (similar performance to the AA alphabet) Different alphabets for CN and SA domains Unexpected explanations: Alphabet reduction clustered AA types that experts did not expect
- 132. Automated Alphabet Reduction Our method produces better reduced alphabets than other reduced alphabets from the literature and than other expert- designed ones Alphabets from the literature Expert designed alphabets Alphabet Letters CN acc. SA acc. Diff. Ref. AA 20 74.0±0.6 70.7±0.4 --- --- Our method 5 73.3±0.5 70.3±0.4 0.7/0.4 [Bacardit et al., 07] WW5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Wang & Wang, 99] SR5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Solis & Rackovsky, 00] MU4 5 72.6±0.7 69.4±0.4 1.4/1.3 [Murphy et al., 00] MM5 6 73.1±0.6 69.3±0.3 0.9/1.4 [Melo & Marti-Renom, 06] HD1 7 72.9±0.6 69.3±0.4 1.1/1.4 [Bacardit et al., 07] HD2 9 73.0±0.6 69.3±0.4 1.0/1.4 [Bacardit et al., 07] HD3 11 73.2±0.6 69.9±0.4 0.8/0.8 [Bacardit et al., 07]
- 133. Efficiency gains fromthe alphabet reduction We have extrapolated the reduced alphabet to the much larger and richer Position-Specific Scoring Matrices (PSSM) representation Accuracy difference is still less than 1% Obtained rule sets are simpler and training process is much faster Performance levels are similar to recent works in the literature [Kinjo et al., 05][Dor and Zhou, 07] Won the bronze medal of the 2007 Humies awards
- 134. Conclusions Bioinformatics containmany challenges that computer science can tackle Optimisation Machine learning Software engineering Evolutionary computation has shown to be very competitive across a large range of bioinformatics problems Facing these challenges for EC has led to the development of many new methods
- 135. References/Bibliography Journals TheBioinformatics Journal BMC Bioinformatics BMC Biodata Mining Bioinformatics books Introduction to Bioinformatics by Arthur Lesk, Oxford University Press. Introduction to Bioinformatics. A. Tramontano, Chapman and Hall/CRC Specialised topics Bioinformatics for –omics data. Methods and Protocols. Bernd Mayer (ed). Springer Next-Generation Sequencing special issue of the Bioinformatics Journal; http://www.oxfordjournals.org/our_journals/bioinformatics/nextge nerationsequencing.html
- 136. References/Bibliography J. Bacardit,M. Stout, J.D. Hirst, N. Krasnogor and J. Blazewicz, Coordination number prediction using Learning Classifier Systems: Performance and interpretability. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 247-254, ACM Press, 2006 Stout, M., Bacardit, J., Hirst, J.D. and Krasnogor, N. Prediction of Recursive Convex Hull Class Assignments for Protein Residues. Bioinformatics, 24(7):916-923, 2008 Stout, M., Bacardit, J., Hirst, J.D., Smith, R.E. and Krasnogor, N. Prediction of Topological Contacts in Proteins Using Learning Classifier Systems. Soft Computing Journal, 13(3):245-258, 2009 J. Bacardit, E.K. Burke and N. Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic Computing journal 1(1):55-67, 2009 J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith and N. Krasnogor. Automated Alphabet Reduction for Protein Datasets. BMC Bioinformatics 10:6, 2009 George W. Bassel, Enrico Glaab, Julietta Marquez, Michael J. Holdsworth and Jaume Bacardit. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets. The Plant Cell, 23(9):3101-3116, 2011 J. Bacardit, P. Widera, A. Márquez-Chamorro, F. Divina, J.S. Aguilar-Ruiz and Natalio Krasnogor. Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features. Bioinformatics first published online July 25, 2012 doi:10.1093/bioinformatics/bts472
- 137. References/Bibliography Jason H.Moore et al., Bioinformatics challenges for genome-wide association studies Bioinformatics (2010) 26(4): 445-455 Loris Nanni, Sheryl Brahnam, Alessandra Lumini, High performance set of PseAAC and sequence based descriptors for protein classification, Journal of Theoretical Biology 266(1):1-10, 2010 Fernando Otero et al., A hierarchical multi-label classification ant colony algorithm for protein function prediction, Memetic Computing 2(3):165-181, 2010 Daniel Barthel et al., Procksi: a decision support system for protein (structure) comparison, knowledge, similarity and information. BMC Bioinformatics, 8:416, 2007 http://omics.org/index.php/Alphabetically_ordered_list_of_omes_and_omics Federico Divina and Jesus S. Aguilar-Ruiz. 2006. Biclustering of Expression Data with Evolutionary Computation. IEEE Trans. on Knowl. and Data Eng. 18, 5 (May 2006), 590- 602. Martinez-Ballesteros, M Nepomuceno-Chamorro, J C Riquelme (2011) Inferring gene- gene associations from Quantitative Association Rules In: 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011 ) 1241 – 1246 Rubén Armañanzas, Iñaki Inza, Roberto Santana, Yvan Saeys, Jose Flores, Jose Lozano, Yves Peer, Rosa Blanco, Víctor Robles, Concha Bielza, Pedro Larrañaga. A review of
- 138. Acknowledgements• Prof. NatalioKrasnogor • Prof. Michael Holdsworth • Prof. Jonathan Hirst • Dr. Michael Stout • Dr. George Bassel • Dr. Enrico Glaab • Dr. Pawel Widera • EPSRC GR/T07534/01 & EP/H016597/1 • EU FP7 CADMAD project
- 139. Dr. Jaume Bacardit InterdisciplinaryComputing and Complex Systems (ICOS) research group University of Nottingham jaume.bacardit@nottingham.ac.uk