Bioinformatics

Bioinformatics (

Computational, statistical, and computer programming techniques have been used for computer simulation analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of genomics, such as by the identification of genes and single nucleotide polymorphisms (SNPs). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. Bioinformatics also includes proteomics, which tries to understand the organizational principles within nucleic acid and protein sequences.^[2]

Image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. Bioinformatics includes text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA,^[3] RNA,^[3][4] proteins^[5] as well as biomolecular interactions.^[6][7][8][9]

History

The first definition of the term bioinformatics was coined by Paulien Hogeweg and Ben Hesper in 1970, to refer to the study of information processes in biotic systems.^{[10][11][12][13][14]} This definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems).^[11]

Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology.

Analyzing biological data to produce meaningful information involves writing and running software programs that use

There has been a tremendous advance in speed and cost reduction since the completion of the Human Genome Project, with some labs able to sequence over 100,000 billion bases each year, and a full genome can be sequenced for $1,000 or less.^[15]

Computers became essential in molecular biology when

In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.[22]^[23]

Goals

In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore^[

Important sub-disciplines within bioinformatics and computational biology include:

• Development and implementation of computer programs to efficiently access, manage, and use various types of information.
• Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences.

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include:

genome-wide association studies, the modeling of evolution and cell division/mitosis.

Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.

Over the past few decades, rapid developments in genomic and other molecular research technologies and developments in

information technologies

have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes.

Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.

Sequence analysis

Main articles: Sequence alignment, Sequence database, and Alignment-free sequence analysis

Since the bacteriophage

molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Computer programs such as BLAST are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billion nucleotides.^[26]

DNA sequencing

Main article: DNA sequencing

Before sequences can be analyzed, they are obtained from a data storage bank, such as GenBank. DNA sequencing is still a non-trivial problem as the raw data may be noisy or affected by weak signals. Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.

Sequence assembly

Main article: Sequence assembly

Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. The

The Institute for Genomic Research (TIGR) to sequence the first bacterial genome, Haemophilus influenzae)^[27] generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 nucleotides long, depending on the sequencing technology). The ends of these fragments overlap and, when aligned properly by a genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. For a genome as large as the human genome

, it may take many days of CPU time on large-memory, multiprocessor computers to assemble the fragments, and the resulting assembly usually contains numerous gaps that must be filled in later. Shotgun sequencing is the method of choice for virtually all genomes sequenced (rather than chain-termination or chemical degradation methods), and genome assembly algorithms are a critical area of bioinformatics research.

See also:

sequence mining, sequence profiling tool, and sequence motif

Genome annotation

Main article: Gene prediction

In genomics, annotation refers to the process of marking the stop and start regions of genes and other biological features in a sequenced DNA sequence. Many genomes are too large to be annotated by hand. As the rate of sequencing exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics^[when?].

Genome annotation can be classified into three levels: the nucleotide, protein, and process levels.

Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms can be successful. Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.

The principal aim of protein-level annotation is to assign function to the protein products of the genome. Databases of protein sequences and functional domains and motifs are used for this type of annotation. About half of the predicted proteins in a new genome sequence tend to have no obvious function.

Understanding the function of genes and their products in the context of cellular and organismal physiology is the goal of process-level annotation. An obstacle of process-level annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.^[28]

The first description of a comprehensive annotation system was published in 1995

The Institute for Genomic Research, which performed the first complete sequencing and analysis of the genome of a free-living (non-symbiotic) organism, the bacterium Haemophilus influenzae.^[27] The system identifies the genes encoding all proteins, transfer RNAs, ribosomal RNAs, in order to make initial functional assignments. The GeneMark program trained to find protein-coding genes in Haemophilus influenzae

is constantly changing and improving.

Following the goals that the Human Genome Project left to achieve after its closure in 2003, the ENCODE project was developed by the National Human Genome Research Institute. This project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).

Gene function prediction

While genome annotation is primarily based on sequence similarity (and thus homology), other properties of sequences can be used to predict the function of genes. In fact, most gene function prediction methods focus on protein sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic amino acids predicts transmembrane segments in proteins. However, protein function prediction can also use external information such as gene (or protein) expression data, protein structure, or protein-protein interactions.^[29]

Computational evolutionary biology

Further information: Computational phylogenetics

Informatics

has assisted evolutionary biologists by enabling researchers to:

• trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
• compare entire
  genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation
  ,
• build complex computational population genetics models to predict the outcome of the system over time^[30]
• track and share information on an increasingly large number of species and organisms

Future work endeavours to reconstruct the now more complex

tree of life.^{[according to whom?}

]

Comparative genomics

Main article: Comparative genomics

The core of comparative genome analysis is the establishment of the correspondence between

Bayesian analysis

of problems based on probabilistic models.

Many of these studies are based on the detection of sequence homology to assign sequences to protein families.^[32]

Pan genomics

Main article: Pan-genome

Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular monophyletic taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.^[33]

Genetics of disease

Main article:

Genome-wide association studies

As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple Mendelian inheritance has been observed for over 3,000 disorders that have been identified at the Online Mendelian Inheritance in Man database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as infertility,^[34] breast cancer^[35] and Alzheimer's disease^[36]), rather than a single cause.^[37][38] There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides. ^[39]

Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability.

rare variants.^[42] Functional annotations predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies.^[43] Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization.^[44][45] Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.^[46]

Analysis of mutations in cancer

Main article: Oncogenomics

In

terabytes of data per experiment. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.^{[citation needed}

]

Two important principles can be used to identify cancer by mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.^[47]

Further improvements in bioinformatics could allow for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.^[48]

Gene and protein expression

Analysis of gene expression

The

epithelial

cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

Analysis of protein expression

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. The former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples when multiple incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays.^[50]

Analysis of regulation

Gene regulation is a complex process where a signal, such as an extracellular signal such as a hormone, eventually leads to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments.

Expression data can be used to infer gene regulation: one might compare

regulatory elements. Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering

methods.

Analysis of cellular organization

Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. A

gene ontology category, cellular component, has been devised to capture subcellular localization in many biological databases

.

Microscopy and image analysis

Microscopic pictures allow for the location of organelles as well as molecules, which may be the source of abnormalities in diseases.

Protein localization

Finding the location of proteins allows us to predict what they do. This is called protein function prediction. For instance, if a protein is found in the nucleus it may be involved in gene regulation or splicing. By contrast, if a protein is found in mitochondria, it may be involved in respiration or other metabolic processes. There are well developed protein subcellular localization prediction resources available, including protein subcellular location databases, and prediction tools.^[51][52]

Nuclear organization of chromatin

Main article: Nuclear organization

Data from high-throughput

Topologically Associating Domains (TADs), that are organised together in three-dimensional space.^[53]

Structural bioinformatics

Main articles: Structural bioinformatics and Protein structure prediction

See also:

Structural domain

Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.^[54][55]

Amino acid sequence

The linear

In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. Homology modeling is used to predict the structure of an unknown protein from existing homologous proteins.

One example of this is hemoglobin in humans and the hemoglobin in legumes (leghemoglobin), which are distant relatives from the same protein superfamily. Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes and shared ancestor.^[56]

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

Another aspect of structural bioinformatics include the use of protein structures for

A 2021

Network analysis seeks to understand the relationships within biological networks such as metabolic or protein–protein interaction networks. Although biological networks can be constructed from a single type of molecule or entity (such as genes), network biology often attempts to integrate many different data types, such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both.

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes that comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

Molecular interaction networks

Tens of thousands of three-dimensional protein structures have been determined by

Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions.

Biodiversity informatics

Biodiversity informatics deals with the collection and analysis of

The enormous number of published literature makes it virtually impossible for individuals to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. For example:

• Abbreviation recognition – identify the long-form and abbreviation of biological terms
• Named-entity recognition – recognizing biological terms such as gene names
• Protein–protein interaction – identify which proteins interact with which proteins from text

The area of research draws from statistics and computational linguistics.

High-throughput image analysis

Computational technologies are used to automate the processing, quantification and analysis of large amounts of high-information-content

• high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics)
• morphometrics
• clinical image analysis and visualization
• determining the real-time air-flow patterns in breathing lungs of living animals
• quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
• making behavioral observations from extended video recordings of laboratory animals
• infrared measurements for metabolic activity determination
• inferring clone overlaps in DNA mapping, e.g. the Sulston score

High-throughput single cell data analysis

Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.

Ontologies and data integration

Biological ontologies are directed acyclic graphs of controlled vocabularies. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.^{[citation needed]}

The

Databases

Databases are essential for bioinformatics research and applications. Databases exist for many different information types, including DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases can contain both empirical data (obtained directly from experiments) and predicted data (obtained from analysis of existing data). They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. Databases can have different formats, access mechanisms, and be public or private.

Some of the most commonly used databases are listed below:

• Used in biological sequence analysis:
  Genbank, UniProt
• Used in structure analysis: Protein Data Bank (PDB)
• Used in finding Protein Families and Motif Finding: InterPro, Pfam
• Used for Next Generation Sequencing: Sequence Read Archive
• Used in Network Analysis: Metabolic Pathway Databases (KEGG, BioCyc), Interaction Analysis Databases, Functional Networks
• Used in design of synthetic genetic circuits: GenoCAD^{[citation needed]}

Software and tools

Software tools for bioinformatics include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by bioinformatics companies or by public institutions.

Open-source bioinformatics software

Many

Open-source bioinformatics software includes Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Orange with its bioinformatics add-on, Apache Taverna, UGENE and GenoCAD.

The non-profit Open Bioinformatics Foundation^[60] and the annual Bioinformatics Open Source Conference promote open-source bioinformatics software.^[61]

Web services in bioinformatics

SOAP- and REST-based interfaces have been developed to allow client computers to use algorithms, data and computing resources from servers in other parts of the world. The main advantage are that end users do not have to deal with software and database maintenance overheads.

Basic bioinformatics services are classified by the

Bioinformatics workflow management systems

A

• provide an easy-to-use environment for individual application scientists themselves to create their own workflows,
• provide interactive tools for the scientists enabling them to execute their workflows and view their results in real-time,
• simplify the process of sharing and reusing workflows between the scientists, and
• enable scientists to track the provenance of the workflow execution results and the workflow creation steps.

Some of the platforms giving this service: Galaxy, Kepler, Taverna, UGENE, Anduril, HIVE.

BioCompute and BioCompute Objects

In 2014, the US Food and Drug Administration sponsored a conference held at the National Institutes of Health Bethesda Campus to discuss reproducibility in bioinformatics.^[63] Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.^[64] These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the Human Variome Project and the European Federation for Medical Informatics, and research institutions including Stanford, the New York Genome Center, and the George Washington University.

It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.^[65]

In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a BioCompute Object, an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.^[66][67]

Education platforms

Bioinformatics is not only taught as in-person masters degree at many universities. The computational nature of bioinformatics lends it to computer-aided and online learning.^[68][69] Software platforms designed to teach bioinformatics concepts and methods include Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. The Canadian Bioinformatics Workshops provides videos and slides from training workshops on their website under a Creative Commons license. The 4273π project or 4273pi project^[70] also offers open source educational materials for free. The course runs on low cost Raspberry Pi computers and has been used to teach adults and school pupils.^[71][72] 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.^[73][74]

MOOC platforms also provide online certifications in bioinformatics and related disciplines, including Coursera's Bioinformatics Specialization (UC San Diego) and Genomic Data Science Specialization (Johns Hopkins) as well as EdX's Data Analysis for Life Sciences XSeries (Harvard).

Conferences

There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), and Research in Computational Molecular Biology (RECOMB).

See also

References

Further reading

• Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
• Achuthsankar S Nair Computational Biology & Bioinformatics – A gentle Overview Archived 16 December 2008 at the Wayback Machine, Communications of Computer Society of India, January 2007
• ISBN 1-58488-406-1
  (Chapman & Hall/Crc Computer and Information Science Series)
• Baldi, P and Brunak, S, Bioinformatics: The Machine Learning Approach, 2nd edition. MIT Press, 2001.
  ISBN 0-262-02506-X
• Barnes, M.R. and Gray, I.C., eds., Bioinformatics for Geneticists, first edition. Wiley, 2003.
  ISBN 0-470-84394-2
• Baxevanis, A.D. and Ouellette, B.F.F., eds., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley, 2005.
  ISBN 0-471-47878-4
• Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds.,
  ISBN 0-471-25093-7
• Cristianini, N. and Hahn, M. Introduction to Computational Genomics Archived 4 January 2009 at the
  ISBN 0-521-67191-4
  )
• Durbin, R., S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998.
  ISBN 0-521-62971-3
• Gilbert D (September 2004). "Bioinformatics software resources". Briefings in Bioinformatics. 5 (3): 300–4.
  PMID 15383216
  .
• Keedwell, E., Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. Wiley, 2005.
  ISBN 0-470-02175-6
• Kohane, et al. Microarrays for an Integrative Genomics. The MIT Press, 2002.
  ISBN 0-262-11271-X
• Lund, O. et al. Immunological Bioinformatics. The MIT Press, 2005.
  ISBN 0-262-12280-4
• ISBN 0-521-85700-7
• Pevzner, Pavel A. Computational Molecular Biology: An Algorithmic Approach The MIT Press, 2000.
  ISBN 0-262-16197-4
• Soinov, L. Bioinformatics and Pattern Recognition Come Together Archived 10 May 2013 at the Wayback Machine Journal of Pattern Recognition Research (JPRR Archived 8 September 2008 at the Wayback Machine), Vol 1 (1) 2006 p. 37–41
• Stevens, Hallam, Life Out of Sequence: A Data-Driven History of Bioinformatics, Chicago: The University of Chicago Press, 2013,
  ISBN 9780226080208
• Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001.
  ISBN 0-596-00080-4
• Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report Archived 28 January 2007 at the Wayback Machine
• Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995) Archived 6 July 2008 at the Wayback Machine
• Foundations of Computational and Systems Biology MIT Course
• Computational Biology: Genomes, Networks, Evolution Free MIT Course Archived 8 April 2013 at the Wayback Machine

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