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Sunday, March 22, 2015

Scientists confirm: Pesticides kill America's honey bees

Published time: July 25, 2013 20:24
Edited time: July 26, 2013 15:36
Original link:  http://rt.com/usa/bee-pesticide-scientist-research-600/

Reuters / Stephane Mahe
Reuters / Stephane Mahe
Honey bees are quickly disappearing from the US – a phenomenon that has left scientists baffled. But new research shows that bees exposed to common agricultural chemicals while pollinating US crops are less likely to resist a parasitic infection.

As a result of chemical exposure, honey bees are more likely to succumb to the lethal Nosema ceranae parasite and die from the resulting complications.

Scientists from the University of Maryland and the US Department of Agriculture on Wednesday published a study that linked chemicals, including fungicides, to the mass die-offs. Scientists have long struggled to find the cause behind the Colony Collapse Disorder (CCD), in which an estimated 10 million beehives at an average value of $200 each have been lost since 2006.

Last winter, the honey bee population declined by 31.1 percent, with some beekeepers reporting losses of 90 to 100 percent of their bee populations. Scientists are concerned that “Beemageddon” could cause the collapse of the $200 billion agriculture industry, since more than 100 US crops rely on honey bees to pollinate them.

The new findings are key in determining one of the causes of the CCD, but they fail to explain why entire beehives sometimes die at once.

UMD and DOA researchers found that pollen samples in fields ranging from Delaware to Maine contained nine different agricultural chemicals, including fungicides, herbicides, insecticides and miticides. One particular sample even contained 21 different agricultural chemicals. To test their theory, they fed pesticide-ridden pollen samples to healthy bees and then infected them with the parasite. They found that the pesticides hindered the bees’ abilities to resist the infection, thus contributing to their deaths. The fungicide chlorothalonil was particularly damaging, tripling the risks of parasitic infection. 
 
“We don’t think of fungicides as having a negative effect on bees, because they’re not designed to kill insects,” Dennis vanEngelsdorp, the study’s senior author, said in a news release.

He explained that federal regulations restrict the use of insecticides while pollinators are foraging, but noted that “there are no such restrictions on fungicides, so you’ll often see fungicide applications going on while bees are foraging on the crop. The finding suggests that we have to reconsider that policy.”

Bees are declining at such a fast rate that one bad winter could trigger an agricultural disaster. California’s almond crop would be hit particularly hard, since the state supplies 80 percent of the world’s almonds. Pollinating California’s 760,000 acres of almond fields requires 1.5 million out-of-state bee colonies, which makes up 60 percent of the country’s beehives. The CCD is a major threat to this $4 billion industry.

Entomologists suspect that a number of other factors also contribute to the CCD, including climate change, habitat destructing and handling practices that expose bees to foreign pathogens. But the effect of agricultural chemicals is particularly alarming, especially since the US does not have laws banning the use of the pesticides that are affecting bee health.
 
“The pesticide issue in itself is much more complex than we have led to believe,” vanEngelsdorp said. “It’s a lot more complicated than just one product, which means of course the solution does not lie in just banning one class of product.”

Can Nanobubbles Save the Planet from Environmental Destruction?

Purple means the baking soda hasn't yet absorbed CO2; yellow indicates a loaded bubble - (Photo by John Vericella/LLNL)
Purple means the baking soda hasn’t yet absorbed CO2; yellow
indicates a loaded bubble – (Photo by John Vericella/LLNL)
New high-tech nano bubbles filled with an age-old kitchen standby may
be the next weapon in the battle against out-of-control carbon dioxide
emissions from power plants.

The project, from Lawrence Livermore National Laboratory, combines an old time-tested idea with a new high-tech idea: put baking soda in tiny, permeable nanobubbles, and then use the bubbles to absorb emissions from power plants directly at the source, before they can enter the atmosphere and contribute to the anthropogenic CO2 emissions currently sending the environment into a self-sustaining warming cycle.

The View Before

With mankind’s voracious appetite for energy still not sated by carbon-neutral generating technologies, it’s a sure bet that combustible hydrocarbons will continue as a mainstay of electrical and mechanical energy generation for the foreseeable future. The carbon dioxide produced by the combustion of these energy sources remains a significant threat to the stability of the global environment as we know it, however. Capturing that gas before it enters the atmosphere and contributes to the phenomena of global warming is a high priority for both industry and climate scientists.

Power plants remain the largest wholesale emitters of anthropogenic CO2. The idea of sequestering their emissions at the source is not a new one, nor is using baking soda as the absorbing agent. Baking soda has long been known to absorb CO2, and commercial sequestration solutions make use of it in systems which tap into exhaust flues and pull the CO2 out of the exhaust before it comes out the top of the stack.

But those systems are cumbersome. One prototype device is the size of two fifty-foot trailers, requiring quite a lot of engineering work to re-route exhaust fumes in order to be of any use. Removing and replacing the baking soda in such a system can be correspondingly difficult.

New Developments

The Lawrence Livermore team’s solution deals with those problems and several others by using the nanobubble approach. The bubbles are tiny and can be packaged and replaced in arbitrary volumes, whatever is easiest for handling. Because the bubbles, made of a synthetic polymer, are permeable to gas but not to liquids, they function with no chance of messy spills. Rather than raw baking soda, they are filled with an aqueous sodium carbonate solution.

The solution has another benefit—it is pH reactive and changes color, from purple to yellow, when the bubble is saturated with CO2, so it’s obvious when the bubbles need to be refreshed.

The tiny size of the spheres also improves the efficiency of the solution. The exposed surface area of the bubbles provides considerably more area for the CO2 to interface with the baking soda for the absorbent reaction to occur. A like volume of baking soda in a more conventional filter system cannot remove as much CO2 as the nanobubbles can over a given period.

The nanobubble system is not yet in use in any plants and may be too cumbersome for other CO2 producers — the volume required, for example, means that it is unlikely to be adapted to anything as small as the common automobile. But the flexible nature of the bubbles may yet result in unexpected venues where they could be used to help sequester CO2 emissions.

How journals like Nature, Cell and Science are damaging science



The incentives offered by top journals distort science, just as big bonuses distort banking 
 
Original link:  http://www.theguardian.com/commentisfree/2013/dec/09/how-journals-nature-science-cell-damage-science
Litter in the street
The journal Science has recently retracted a high-profile paper reporting links between littering and violence. Photograph: Alamy/Janine Wiedel Janine Wiedel Photolibrary/Alamy
I am a scientist. Mine is a professional world that achieves great things for humanity. But it is disfigured by inappropriate incentives. The prevailing structures of personal reputation and career advancement mean the biggest rewards often follow the flashiest work, not the best. Those of us who follow these incentives are being entirely rational – I have followed them myself – but we do not always best serve our profession's interests, let alone those of humanity and society.

We all know what distorting incentives have done to finance and banking. The incentives my colleagues face are not huge bonuses, but the professional rewards that accompany publication in prestigious journals – chiefly Nature, Cell and Science.

These luxury journals are supposed to be the epitome of quality, publishing only the best research. Because funding and appointment panels often use place of publication as a proxy for quality of science, appearing in these titles often leads to grants and professorships. But the big journals' reputations are only partly warranted. While they publish many outstanding papers, they do not publish only outstanding papers. Neither are they the only publishers of outstanding research.

These journals aggressively curate their brands, in ways more conducive to selling subscriptions than to stimulating the most important research. Like fashion designers who create limited-edition handbags or suits, they know scarcity stokes demand, so they artificially restrict the number of papers they accept. The exclusive brands are then marketed with a gimmick called "impact factor" – a score for each journal, measuring the number of times its papers are cited by subsequent research. Better papers, the theory goes, are cited more often, so better journals boast higher scores. Yet it is a deeply flawed measure, pursuing which has become an end in itself – and is as damaging to science as the bonus culture is to banking.

It is common, and encouraged by many journals, for research to be judged by the impact factor of the journal that publishes it. But as a journal's score is an average, it says little about the quality of any individual piece of research. What is more, citation is sometimes, but not always, linked to quality. A paper can become highly cited because it is good science – or because it is eye-catching, provocative or wrong. Luxury-journal editors know this, so they accept papers that will make waves because they explore sexy subjects or make challenging claims. This influences the science that scientists do. It builds bubbles in fashionable fields where researchers can make the bold claims these journals want, while discouraging other important work, such as replication studies.

In extreme cases, the lure of the luxury journal can encourage the cutting of corners, and contribute to the escalating number of papers that are retracted as flawed or fraudulent. Science alone has recently retracted high-profile papers reporting cloned human embryos, links between littering and violence, and the genetic profiles of centenarians.
Perhaps worse, it has not retracted claims that a microbe is able to use arsenic in its DNA instead of phosphorus, despite overwhelming scientific criticism.

There is a better way, through the new breed of open-access journals that are free for anybody to read, and have no expensive subscriptions to promote. Born on the web, they can accept all papers that meet quality standards, with no artificial caps. Many are edited by working scientists, who can assess the worth of papers without regard for citations. As I know from my editorship of eLife, an open access journal funded by the Wellcome Trust, the Howard Hughes Medical Institute and the Max Planck Society, they are publishing world-class science every week.

Funders and universities, too, have a role to play. They must tell the committees that decide on grants and positions not to judge papers by where they are published. It is the quality of the science, not the journal's brand, that matters. Most importantly of all, we scientists need to take action. Like many successful researchers, I have published in the big brands, including the papers that won me the Nobel prize for medicine, which I will be honoured to collect tomorrow.. But no longer. I have now committed my lab to avoiding luxury journals, and I encourage others to do likewise.

Just as Wall Street needs to break the hold of the bonus culture, which drives risk-taking that is rational for individuals but damaging to the financial system, so science must break the tyranny of the luxury journals. The result will be better research that better serves science and society.

Saturday, March 21, 2015

Cheminformatics


From Wikipedia, the free encyclopedia

Cheminformatics (also known as chemoinformatics, chemioinformatics and chemical informatics) is the use of computer and informational techniques applied to a range of problems in the field of chemistry. These in silico techniques are used in, for example, pharmaceutical companies in the process of drug discovery. These methods can also be used in chemical and allied industries in various other forms.

History

The term chemoinformatics was defined by F.K. Brown [1][2] in 1998:
Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.
Since then, both spellings have been used, and some have evolved to be established as Cheminformatics,[3] while European Academia settled in 2006 for Chemoinformatics.[4] The recent establishment of the Journal of Cheminformatics is a strong push towards the shorter variant.

Basics

Cheminformatics combines the scientific working fields of chemistry, computer science and information science for example in the areas of topology, chemical graph theory, information retrieval and data mining in the chemical space.[5][6][7][8] Cheminformatics can also be applied to data analysis for various industries like paper and pulp, dyes and such allied industries.

Applications

Storage and retrieval

The primary application of cheminformatics is in the storage, indexing and search of information relating to compounds. The efficient search of such stored information includes topics that are dealt with in computer science as data mining, information retrieval, information extraction and machine learning. Related research topics include:

File formats

The in silico representation of chemical structures uses specialized formats such as the XML-based Chemical Markup Language or SMILES. These representations are often used for storage in large chemical databases. While some formats are suited for visual representations in 2 or 3 dimensions, others are more suited for studying physical interactions, modeling and docking studies.

Virtual libraries

Chemical data can pertain to real or virtual molecules. Virtual libraries of compounds may be generated in various ways to explore chemical space and hypothesize novel compounds with desired properties.

Virtual libraries of classes of compounds (drugs, natural products, diversity-oriented synthetic products) were recently generated using the FOG (fragment optimized growth) algorithm. [9] This was done by using cheminformatic tools to train transition probabilities of a Markov chain on authentic classes of compounds, and then using the Markov chain to generate novel compounds that were similar to the training database.

Virtual screening

In contrast to high-throughput screening, virtual screening involves computationally screening in silico libraries of compounds, by means of various methods such as docking, to identify members likely to possess desired properties such as biological activity against a given target. In some cases, combinatorial chemistry is used in the development of the library to increase the efficiency in mining the chemical space. More commonly, a diverse library of small molecules or natural products is screened.

Quantitative structure-activity relationship (QSAR)

This is the calculation of quantitative structure-activity relationship and quantitative structure property relationship values, used to predict the activity of compounds from their structures. In this context there is also a strong relationship to Chemometrics. Chemical expert systems are also relevant, since they represent parts of chemical knowledge as an in silico representation. There is a relatively new concept of Matched molecular pair analysis or Predcition driven MMPA which is coupled with QSAR model in order to identify activity cliff.[10]

Bioinformatics


From Wikipedia, the free encyclopedia


Map of the human X chromosome (from the NCBI website). Assembly of the human genome is one of the greatest achievements of bioinformatics.

Bioinformatics Listeni/ˌb.ˌɪnfərˈmætɪks/ is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data.

Bioinformatics is both an umbrella term for the body of biological studies that use computer programming as part of their methodology, as well as a reference to specific analysis "pipelines" that are repeatedly used, particularly in the fields of genetics and genomics. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences.

Introduction

Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the 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 the comparison of 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, RNA, and protein structures as well as molecular interactions.

History

Paulien Hogeweg and Ben Hesper coined the term bioinformatics in 1970 to refer to the study of information processes in biotic systems.[1][2][3] This definition placed bioinformatics as a field parallel to biophysics (the study of physical processes in biological systems) or biochemistry (the study of chemical processes in biological systems).[1]

Sequences

Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early 1950s. Comparing multiple sequences manually turned out to be impractical. A pioneer in the field was Margaret Oakley Dayhoff, who has been hailed by David Lipman, director of the National Center for Biotechnology Information, as the "mother and father of bioinformatics."[4] Dayhoff compiled one of the first protein sequence databases, initially published as books[5] and pioneered methods of sequence alignment and molecular evolution.[6] Another early contributor to bioinformatics was Elvin A. Kabat, who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released with Tai Te Wu between 1980 and 1991.[7]

Genomes

As whole genome sequences became available, again with the pioneering work of Frederick Sanger,[8] it became evident that computer-assisted analysis would be insightful. The first analysis of this type, which had important input from cryptologists at the National Security Agency, was applied to the nucleotide sequences of the bacteriophages MS2 and PhiX174. As a proof of principle, this work showed that standard methods of cryptology could reveal intrinsic features of the genetic code such as the codon length and the reading frame. This work seems to have been ahead of its time—it was rejected for publication by numerous standard journals and finally found a home in the Journal of Theoretical Biology.[9] The term bioinformatics was re-discovered and used to refer to the creation of databases such as GenBank in 1982. With public availability of data, tools for their analysis were quickly developed and described in journals, such as Nucleic Acids Research, which published specialized issues on bioinformatics tools as early as 1982.

Goals

To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[10] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include:
  • Development and implementation of computer programs that enable efficient access to, use and management of, various types of information
  • Development of new algorithms (mathematical formulas) and statistical measures that 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, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, and the modeling of evolution.

Bioinformatics now 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.

Approaches[edit]

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.

There are two fundamental ways of modelling a Biological system (e.g., living cell) both coming under Bioinformatic approaches.
  • Static
    • Sequences – Proteins, Nucleic acids and Peptides
    • Interaction data among the above entities including microarray data and Networks of proteins, metabolites
  • Dynamic
    • Structures – Proteins, Nucleic acids, Ligands (including metabolites and drugs) and Peptides (structures studied with bioinformatics tools are not considered static anymore and their dynamics is often the core of the structural studies)
    • Systems Biology comes under this category including reaction fluxes and variable concentrations of metabolites
    • Multi-Agent Based modelling approaches capturing cellular events such as signalling, transcription and reaction dynamics
A broad sub-category under bioinformatics is structural bioinformatics.

Relation to other fields

Bioinformatics is a science field that is similar to but distinct from biological computation and computational biology. Biological computation uses bioengineering and biology to build biological computers, whereas bioinformatics uses computation to better understand biology. Bioinformatics and computational biology have similar aims and approaches, but they differ in scale: bioinformatics organizes and analyzes basic biological data, whereas computational biology builds theoretical models of biological systems, just as mathematical biology does with mathematical models.

Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics.

Sequence analysis


The sequences of different genes or proteins may be aligned side-by-side to measure their similarity. This alignment compares protein sequences containing WPP domains.

Since the Phage Φ-X174 was sequenced in 1977,[11] the DNA sequences of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encode proteins, RNA genes, regulatory sequences, structural motifs, and repetitive sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs such as BLAST are used daily to search sequences from more than 260 000 organisms, containing over 190 billion nucleotides.[12] These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae)[13] does not produce entire chromosomes. Instead it 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 today, and genome assembly algorithms are a critical area of bioinformatics research.

Another aspect of bioinformatics in sequence analysis is annotation. This involves computational gene finding to search for protein-coding genes, RNA genes, and other functional sequences within a genome. Not all of the nucleotides within a genome are part of genes. Within the genomes of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects — for example, in the use of DNA sequences for protein identification.

Genome annotation

In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. This process needs to be automated because most genomes are too large to annotate by hand, not to mention the desire to annotate as many genomes as possible, as the rate of sequencing has ceased to pose a bottleneck. Annotation is made possible by the fact that genes have recognisable start and stop regions, although the exact sequence found in these regions can vary between genes.
The first genome annotation software system was designed in 1995 by Owen White, who was part of the team at The Institute for Genomic Research that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. White built a software system to find the genes (fragments of genomic sequence that encode proteins), the transfer RNAs, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA, such as the GeneMark program trained and used to find protein-coding genes in Haemophilus influenzae, are constantly changing and improving.

Computational evolutionary biology

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. 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,
  • more recently, 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 models of populations to predict the outcome of the system over time[14]
  • 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.

The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related.

Comparative genomics

The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov Chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.
Many of these studies are based on the homology detection and protein families computation.

Genetics of disease

With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases such as infertility,[15] breast cancer [16] or Alzheimer's Disease.[17] Genome-wide association studies are essential to pinpoint the mutations for such complex diseases.[18]

Analysis of mutations in cancer

In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. New physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single-nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. 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.
Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

Gene and protein expression

Analysis of gene expression

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous 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. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; 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 where multiple, but incomplete peptides from each protein are detected.

Analysis of regulation

Regulation is the complex orchestration of events starting with an extracellular signal such as a hormone and leading 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, promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering methods such as the Bi-CoPaM. The later, namely Bi-CoPaM, has been actually proposed to address various issues specific to gene discovery problems such as consistent co-expression of genes over multiple microarray datasets.[19][20]

Structural bioinformatics

Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy – a.k.a. Mad Cow Diseaseprion.) Knowledge of this structure is vital in understanding the function of the protein. Structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.
One of the key ideas in bioinformatics is the notion of homology. 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 the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.

One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.

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

Network and systems biology

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


Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein-protein interactions from Treponema pallidum, the causative agent of syphilis and other diseases.

Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing protein–protein interaction experiments. A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.

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.

Others

Literature analysis

The growth in the number of published literature makes it virtually impossible 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 accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research.
Some examples are:
  • 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.

Biodiversity informatics

Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases, or microbiome data. Examples of such analyses include phylogenetics, niche modelling, species richness mapping, or species identification tools.

Databases

Databases are essential for bioinformatics research and applications. There is a huge number of available databases covering almost everything from DNA and protein sequences, molecular structures, to phenotypes and biodiversity. Databases generally fall into one of three types. Some contain data resulting directly from empirical methods such as gene knockouts. Others consist of predicted data, and most contain data from both sources. There are meta-databases that incorporate data compiled from multiple other databases. Some others are specialized, such as those specific to an organism. These databases vary in their format, way of accession and whether they are public or not. Some of the most commonly used databases are listed below. For a more comprehensive list, please check the link at the beginning of the subsection. Please keep in mind that this is a quick sampling and generally most computation data is supported by wet lab data as well.

Software and tools

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.

Open-source bioinformatics software

Many free and open-source software tools have existed and continued to grow since the 1980s.[21] The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have helped to create opportunities for all research groups to contribute to both bioinformatics and the range of open-source software available, regardless of their funding arrangements. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications. They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration.

The range of open-source software packages includes titles such as Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Apache Taverna, and UGENE. To maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation[21] have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.[22]

Web services in bioinformatics

SOAP- and REST-based interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads.

Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis).[23] The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.

Bioinformatics workflow management systems

A Bioinformatics workflow management system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a Bioinformatics application. Such systems are designed to
  • 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.
  • 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.

Education platforms

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.

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), Research in Computational Molecular Biology (RECOMB) and American Society of Mass Spectrometry (ASMS).

Teacher

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Teacher A teacher in a classroom at a secondary school in ...