Blastx: from gi to description

I have done a blastx and use the outfmt 6 option, because I want to work in the tables format. When I do this I get subject ids of the type


et cetera.

However, in this blastx output format I cannot find any option that will give me the descriptions of these blast hits. Could anyone advice me how I can link these subject ids with descriptions of the sequences, e.g., 'amino acid transporter ANTL1-like'? I'd be much obliged.


7 Command Line BLAST

Given one or more query sequences (usually in FASTA format), BLAST looks for matching sequence regions between them and a subject set.

A sufficiently close match between subsequences (denoted by arrows in the figure above, though matches are usually longer than illustrated here) is called a high-scoring pair (HSP), while a query sequence is said to hit a target sequence if they share one or more HSPs. Sometimes, however, the term “hit” is used loosely, without differentiating between the two. Each HSP is associated with a “bitscore” that is based on the similarity of the subsequences as determined by a particular set of rules. Because in larger subject sets some good matches are likely to be found by chance, each HSP is also associated with an “E value,” representing the expected number of matches one might find by chance in a subject set of that size with that score or better. For example, an E value of 0.05 means that we can expect a match by chance in 1 in 20 similar searches, whereas an E value of 2.0 means we can expect 2 matches by chance for each similar search.

BLAST is not a single tool, but rather a suite of tools (and the suite grows over the years as more features and related tools are added). The most modern version of the software, called BLAST+, is maintained by the National Center for Biotechnology Information (NCBI) and may be downloaded in binary and source forms at

This chapter only briefly covers running BLAST on the command line in simple ways. Reading the help information (e.g., with blastn --help ) and the NCBI BLAST Command Line Applications User Manual at is highly recommended. The NCBI manual covers quite a few powerful and handy features of BLAST on the command line that this book does not.

Overview of the Digestive System

The digestive system, which extends from the mouth to the anus, is responsible for receiving food, breaking it down into nutrients (a process called digestion), absorbing the nutrients into the bloodstream, and eliminating the indigestible parts of food from the body. The digestive tract consists of the

The digestive system also includes organs that lie outside the digestive tract:

The digestive system is sometimes called the gastrointestinal system, but neither name fully describes the system’s functions or components. The organs of the digestive system also produce blood clotting factors and hormones unrelated to digestion, help remove toxic substances from the blood, and chemically alter (metabolize) drugs.

The abdominal cavity is the space that holds the digestive organs. It is bordered by the abdominal wall (composed of layers of skin, fat, muscle, and connective tissue) in front, the spinal column in back, the diaphragm above, and the pelvic organs below. It is lined, as is the outer surface of the digestive organs, by a membrane called the peritoneum.

Experts have recognized a powerful connection between the digestive system and the brain. For example, psychologic factors greatly influence contractions of the intestine, secretion of digestive enzymes, and other functions of the digestive system. Even susceptibility to infection, which leads to various digestive system disorders, is strongly influenced by the brain. In turn, the digestive system influences the brain. For example, long-standing or recurring diseases such as irritable bowel syndrome, ulcerative colitis, and other painful diseases affect emotions, behaviors, and daily functioning. This two-way association has been called the brain-gut axis.

Aging may also affect how the digestive system functions (see Effects of Aging on the Digestive System).


The CGView Server is a comparative genomics tool for circular genomes (plasmid, bacterial, mitochondrial and chloroplast) that allows sequence feature information to be visualized in the context of sequence analysis results and sequence similarity plots. The server seamlessly integrates several sequence analysis procedures and tools with the CGView genome visualization program. The server accepts a variety of commonly used data formats, and generates high-quality, fully labelled graphical maps.

One drawback of the CGView Server compared to standalone tools like ACT is that the server returns static images. Although these images are suitable for publication, ACT may be more useful for in-depth exploration of sequences and BLAST results. To partially overcome the limitations of providing static images, the CGView Server includes an option for generating zoomed maps. Another limitation for some users may be the inability of the CGView Server to generate more conventional linear maps. The web-based Microbial Genome Viewer can be used to generate circular or linear maps, and may be more appropriate for some users.

Despite these limitations, maps generated by the CGView Server can be used to aid in the identification of conserved or diverged genome segments, instances of horizontal gene transfer, and differences in gene copy number. Because a collection of sequences can be used in place of a comparison genome, maps can be used to identify sequences that are part of a particular family, or to visualize regions of a known genome covered by newly obtained sequence reads. Sample maps and data sets further illustrating applications of the CGView Server are available at

GI enzymes and Their Importance in Digestion

The digestive system is very complicated but important to understand from a medical standpoint. Not only is this system import for nutrition but also for immunity. The human body is one amazing complex and to understand how every system works is very important. In the past weeks, our blog has explained the functions of the GI system but this week we are going to focus on the small, unpopular functions also known as the GI enzymes.

Enzymes are substances produced by a living organism that acts as a catalyst to bring about a specific biochemical reaction (List of Digestive Enzymes and Functions, n.d.). There are enzymes in each part of your digestion system and all have a specific function. Let’s create a scenario: You are about to eat a meal that contains a steak, mashed potatoes, and a warm roll. LET THE DIGESTION BEGIN!

Here are all the digestive enzymes in the mouth and their function

1. Ptyalin – Converts starch to simple soluble sugars

2. Amylase – Converts starch to soluble sugars

3. Betaine – Maintains cell fluid balance as osmolytes

4. Bromelain – Anti-inflammatory agent, tenderizes meat

So before you even swallow your bite of this meal, you begin digesting. (List of Digestive Enzymes and Functions, n.d.). Now anatomy and physiology explains to us that the next place for the food to stop is the stomach.

Here are all the digestive enzymes in the stomach and their functions:

1 Pepsin is the main gastric enzyme. It breaks proteins into smaller peptide fragments.

2 Gelatinase, degrades type I and type V gelatin and type IV and V collagen, which are proteoglycans in meat.

3 Gastric amylase degrades starch, but is of minor significance.

4 Gastric lipase is a tributyrase by its biochemical activity, as it acts almost exclusively on tributyrin, a butter fat enzyme.

5 Pepsin enzyme is secreted by gastric glands

6 Renin enzyme change the liquid milk to solid (List of Digestive Enzymes and Functions, n.d.).

From the stomach the food then travels to the small intestines. The small villi in the intestines are what suck up all the nutrients from the enzymes breaking down the food we digest.

Here are the digestive enzymes for the small intestine and their functions:

1. Cholecystokinin – Stimulates digestion of proteins and fats

2. Secretin – Controls secretion of duodenum and osmoregulation

3. Sucrase – Converts sucrose to disaccharides and monosaccharides

4. Maltase – Converts maltose to glucose

5. Lactase – Converts lactose to glucose and galactose

6. Isomaltase – Converts maltose to isomaltose (List of Digestive Enzymes and Functions, n.d.).

We also need to remember other organs help aid in the digestion of food. The pancreas is one those organs.

Here is a list of the pancreatic enzymes and their functions:

1. Pancreatic lipase – Degrades triglycerides into fatty acids and glycerol

2. Chymotrypsin – Converts proteins to aromatic amino acids

3. Carboxypeptidase – Degradation of proteins to amino acids

4. Pancreatic amylase – Degradation of carbohydrates to simple sugars

5. Elastases – Degrade the protein elastin

6. Nucleases – Conversion of nucleic acids to nucleotides and nucleosides

7. Trypsin – Converts proteins to basic amino acids

8. Steapsin – Breakdown of triglycerides to glycerol and fatty acids

9. Phospholipase – Hydrolyzes phospholipids into fatty acids and lipophilic substances (List of Digestive Enzymes and Functions, n.d.).

Now that all the enzymes has been addressed and their functions explained, one can truly understand how complex and fascinating the digestive system is and how it works. With the understanding of the enzymes, we can better address certain issues that everyday people have with digestion and help educated every to healthy and happy stomach and digestive system.


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, 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 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, [2] RNA, [2] [3] proteins [4] as well as biomolecular interactions. [5] [6] [7] [8]

History Edit

Historically, the term bioinformatics did not mean what it means today. Paulien Hogeweg and Ben Hesper coined it in 1970 to refer to the study of information processes in biotic systems. [9] [10] [11] This definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems). [9]

Sequences Edit

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. [12] She compiled one of the first protein sequence databases, initially published as books [13] and pioneered methods of sequence alignment and molecular evolution. [14] 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. [15] 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 thus proof of the concept that bioinformatics would be insightful. [16] [17]

Goals Edit

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 also includes nucleotide and amino acid sequences, protein domains, and protein structures. [18] 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, management and use 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, the modeling of evolution and cell division/mitosis.

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.

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.

Relation to other fields Edit

Bioinformatics is a science field that is similar to but distinct from biological computation, while it is often considered synonymous to computational biology. Biological computation uses bioengineering and biology to build biological computers, whereas bioinformatics uses computation to better understand biology. Bioinformatics and computational biology involve 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 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.

Since the Phage Φ-X174 was sequenced in 1977, [19] 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. 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. [20]

DNA sequencing Edit

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

Sequence assembly Edit

Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research (TIGR) to sequence the first bacterial genome, Haemophilus influenzae) [21] 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 [ when? ] , and genome assembly algorithms are a critical area of bioinformatics research.

Genome annotation Edit

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 description of a comprehensive genome annotation system was published in 1995 [21] by the team at The Institute for Genomic Research that performed the first complete sequencing and analysis of the genome of a free-living organism, the bacterium Haemophilus influenzae. [21] Owen White designed and built a software system to identify the genes encoding all proteins, transfer RNAs, ribosomal RNAs (and other sites) and to make initial functional assignments. 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.

Following the goals that the Human Genome Project left to achieve after its closure in 2003, a new project developed by the National Human Genome Research Institute in the U.S appeared. The so-called ENCODE 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).

Computational evolutionary biology Edit

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,
  • 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 [22]
  • 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? ]

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 Edit

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. [23] 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 spectrum 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 detection of sequence homology to assign sequences to protein families. [24]

Pan genomics Edit

Pan genomics is a concept introduced in 2005 by Tettelin and Medini which eventually took root in bioinformatics. Pan genome is the complete gene repertoire of a particular 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: Set of genes common to all the genomes under study (These are often housekeeping genes vital for survival) and The Dispensable/Flexible Genome: 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. [25]

Genetics of disease Edit

With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases infertility, [26] breast cancer [27] or Alzheimer's disease. [28] Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases. [29] Through these studies, thousands of DNA variants have been identified that are associated with similar diseases and traits. [30] Furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis. [31]

Analysis of mutations in cancer Edit

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.

Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of 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. [32]

With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably. This could create a more flexible process 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. [33]

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

Analysis of gene expression Edit

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. [34] 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 Edit

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. Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays. [35]

Analysis of regulation Edit

Gene regulation is the complex orchestration of events by which a signal, potentially 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 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 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.

Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. This is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems. A gene ontology category, cellular component, has been devised to capture subcellular localization in many biological databases.

Microscopy and image analysis Edit

Microscopic pictures allow us to locate both organelles as well as molecules. It may also help us to distinguish between normal and abnormal cells, e.g. in cancer.

Protein localization Edit

The localization of proteins helps us to evaluate the role of a protein. 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. Protein localization is thus an important component of protein function prediction. There are well developed protein subcellular localization prediction resources available, including protein subcellular location databases, and prediction tools. [36] [37]

Nuclear organization of chromatin Edit

Data from high-throughput chromosome conformation capture experiments, such as Hi-C (experiment) and ChIA-PET, can provide information on the spatial proximity of DNA loci. Analysis of these experiments can determine the three-dimensional structure and nuclear organization of chromatin. Bioinformatic challenges in this field include partitioning the genome into domains, such as Topologically Associating Domains (TADs), that are organised together in three-dimensional space. [38]

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 (mad cow disease) prion.) 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. [ citation needed ]

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 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. [39]

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 Virtual Screening models such as Quantitative Structure-Activity Relationship models and proteochemometric models (PCM). Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and in silico mutagenesis studies.

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 Edit

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.

Literature analysis Edit

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 Edit

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)
  • 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 Edit

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 Edit

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, DNA barcoding, or species identification tools.

Ontologies and data integration Edit

Biological ontologies are directed acyclic graphs of controlled vocabularies. They are designed to capture biological concepts and descriptions in a way that can be easily categorised and analysed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.

The OBO Foundry was an effort to standardise certain ontologies. One of the most widespread is the Gene ontology which describes gene function. There are also ontologies which describe phenotypes.

Databases are essential for bioinformatics research and applications. Many databases exist, covering various information types: for example, DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases may contain empirical data (obtained directly from experiments), predicted data (obtained from analysis), or, most commonly, both. They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. These databases vary in their format, access mechanism, 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.

  • 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

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 Edit

Many free and open-source software tools have existed and continued to grow since the 1980s. [40] 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, Orange with its bioinformatics add-on, Apache Taverna, UGENE and GenoCAD. To maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation [40] have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000. [41]

An alternative method to build public bioinformatics databases is to use the MediaWiki engine with the WikiOpener extension. This system allows the database to be accessed and updated by all experts in the field. [42]

Web services in bioinformatics Edit

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). [43] 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 Edit

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, and
  • enable scientists to track the provenance of the workflow execution results and the workflow creation steps.

BioCompute and BioCompute Objects Edit

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. [44] Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm. [45] 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. [46]

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. [47] [48]

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 [49] 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. [50] [51] 4273π 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. [52] [53]

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). University of Southern California offers a Masters In Translational Bioinformatics focusing on biomedical applications.

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).

Author information


Department of Biological Sciences, University of Southern MS, Hattiesburg, 39406, USA

Mehdi Pirooznia & Youping Deng

Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Rd, Vicksburg, MS, 39180, USA

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Corresponding author

Function of Stomach (With Diagram)| Digestive System

In this article we will discuss about the function of stomach.

1. Temporary storage organ:

It acts as a temporary storage organ. Because of this, the frequency of eating is reduced.

2. Secretory function (Fig. 5.9):

It secretes HCl and pepsin apart from other things including mucus. The G cells of pyloric region secrete gastrin hormone which is one of the GI tract hormones.

3. Digestive function is because of pepsin enzyme. It is a proteolytic enzyme.

4. Protective function is because of high acidic medium due to presence of HCl, many of the micro­organisms die. Thereby it protects the GI tract from getting invaded by the microorganisms.

5. Hemopoietic function is because of the intrinsic factor which is secreted by gastric glands. Intrinsic factor is essential for absorption of vitamin B12 in the ileum region.

6. Absorptive function is also one of the functions of stomach. Some amount of water and alcohol is absorbed in the stomach region.

Motor Functions of Stomach:

The movement of stomach serves important objectives namely:

1. It enables the stomach to act as a temporary reservoir of food.

2. The movements of stomach converts solid food into a fluid paste called chyme and delivers this in small quantities to duodenum for proper digestion in small intestine.

Anatomical basis of gastric motility:

The movement of stomach depends on the arrangement of smooth muscle in the wall.

The arrangement is as follows:

a. Outer longitudinal muscle layer.

b. Inner circular muscle layer.

c. Oblique muscle which is inner to the circular muscle layer and restricted to upper part of stomach.

d. The muscularis mucosa present in the submucosa.

All the muscles are supplied by vagus and sympathetic fibers.

Innervation of Stomach:

It is through the right and left vagi. The preganglionic fibers arise from the dorsal motor nucleus of vagus in medulla. The ganglia are found in myenteric and submucosal plexus. Postganglionic fibers are very short and supply mucous membranes, gastric glands and muscles.

i. Left vagus supplies anterior aspect of stomach

ii. Right vagus supplies posterior aspect of stomach

iii. The efferent fibers are secertomotor to the stomach. Vagus also contains afferent fibers which carry afferent impulses from the stomach to the medulla.

Stimulation of efferent vagi supplying the stomach increases the volume of gastric secretion which is rich in HCl, pepsinogen and also increases the contraction of the gastric muscles. This enhances the peristaltic contractions in stomach and hence emptying of the contents of stomach.

Sympathetic nerve supply are from lateral horn cells of T5-T10 segments of spinal cord. These are preganglionic fibers. Few of them synapse in the ganglia of the sympathetic chain while most of them synapse with cells of celiac ganglion. The post­ganglionic fibers accompany the arterial supply to the stomach.

Effects of sympathetic stimulation on gastric secretion are not definite. Indirectly, it diminishes gastric secretion by reducing blood flow by bringing about vasoconstriction. It is also known that sympathetic stimulation causes an increased alkaline mucoid secretion from the glands. Like vagus sympathetic nerves also carry efferent and afferent fibers to and from the stomach.

Blastx: from gi to description - Biology

Although extensively distributed world-wide, the fig wax scale, Ceroplastes rusci (Linnaeus), was first discovered in Florida at several nursery and stock dealers in 1994 and 1995. It has been a pest of Ixora spp. and infrequently found on other host plants. Prior to the Florida discoveries, the California Department of Food and Agriculture had intercepted specimens from Florida.

Figure 1. Adult female fig wax scales, Ceroplastes rusci (Linnaeus). Photograph by Doug Caldwell, University of Florida.

Distribution (Back to Top)

Talhouk (1975) reported the presence of this scale in the Mediterranean region (Algeria, Cyprus, Egypt, Greece, Israel, Italy, Lebanon, Morocco, Spain, Tunisia and Turkey) and Argentina.

More recent reports also list:

Africa: Algeria, Angola, Canary Islands, Cape Verde Islands, Egypt, Eritrea, Ethiopia, Ghana, Kenya, Libya, Madeira, Morocco, Principe, Sao Tome, Senegal, South Africa, Sudan, Tanzania, Tunisia, Zambia, Zimbabwe

Asia: Afghanistan, India (Bihar, Karnataka, Kerala), Iran, Iraq, Israel, Jordan, Lebanon, Saudi Arabia, Syria, United Arab Emirates, Vietnam

Australasia and Pacific Islands: Australia (Northern Territory), Papua

Central America and Caribbean: Antigua, Dominican Republic, Puerto Rico, Virgin Islands

Europe: Albania, Azores, Balearic Islands, Corsica, Crete, Cyprus, France, Gibraltar, Greece, Italy, Malta, Portugal, Sardinia, Sicily, Spain, Turkey, Yugoslavia

South America: Argentina, Brazil, Guyana, Uruguay

(Ben-Dov 1993, CABI 2011, Vu et al. 2006).

In North America, it appears that it is only established in Florida (United States) (Hodges et al. 2005).

Description (Back to Top)

This scale is deeply encased in pinkish-gray wax, which is divided into three wax plates on each side with additional plates at the anterior and posterior ends. The single large dorsal plate has a central nucleus. Dorsal and lateral plates are separated from each other by dark red lines which are the color of the scale's body beneath the wax. The anterolateral and mediolateral plates have some white wax which indicates the stigmatic wax bands.

Figure 2. Adult female fig wax scale, Ceroplastes rusci (Linnaeus). Photograph by Division of Plant Industry, Florida Department of Agriculture and Consumer Services.

Biology (Back to Top)

The biology of the fig wax scale has not been studied in Florida but has been described on fig trees in Israel (Bodkin 1927). In general, adult females overwinter on twigs and produce eggs very early in the spring. The eggs hatch to crawlers which move to feed on leaves. After about one month, the crawlers molt to 2nd instar nymphs and migrate to the leaf petioles or to new shoots. Maturity is attained in the summer, and a new generation of crawlers is produced. These nymphs mature late in the fall, overwinter on the twigs, and repeat the cycle (Bodkin 1927). Swailem and Awadallah (1973) reported scales to be equally present on both upper and lower leaf surfaces on fig trees in Egypt.

Figure 3. Nymph of the fig wax scale, Ceroplastes rusci (Linnaeus). Photograph by Division of Plant Industry, Florida Department of Agriculture and Consumer Services.

Host Plants (Back to Top)

The fig wax scale has been reported on a broad range of host plants, including the following families:

  • Anacardiaceae (Mangifera indica, Schinus terebinthifolius)
  • Annonaceae (Annona cherimoya, Annona muricata, Annona squamosa)
  • Apocynaceae (Nerium oleander, Thevetia peruviana)
  • Aquifoliaceae (Ilex aquifolium)
  • Araliaceae (Hedera helix)
  • Balsaminaceae (Impatiens sultani)
  • Compositae (Artemisia spp.)
  • Convolvulaceae (Convolvulus spp., Ipomoea batatus)
  • Euphorbiaceae (Euphorbia longan)
  • Lauraceae (Laurus nobilis, Persia americana)
  • Moraceae (Ficus sp., Morus alba, Morus nigra)
  • Musaceae (Musa cavendishi, Musa sapientum)
  • Myrtaceae (Myrtus communis, Psidium guajava)
  • Palmae (Chamaerops humilis)
  • Pittosporaceae (Pittosporum tobira)
  • Platanaceae (Platanus orientalis)
  • Proteaceae (Grevillea robusta)
  • Rosaceae (Crataegus vulgaris, Prunus dulcis, Pyrus communis)
  • Rutaceae (Citrus aurantium, Citrus limon, Citrus paradisi)
  • Sapindaceae (Litchi chinensis, Nephelium lappaceum, Sapindus saponaria)
  • Sebestenaceae (Cordia myxa)
  • Strellitziaceae (Strelitzia reginae)
  • Vitidaceae (Vitis vinifera) (Ben-Dov 1993)

The fig wax scale has also been found feeding on Citrus sinensis and Citrus reticulata in Greece (Argyriou and Mourikis 1981). In Florida, specimens of this scale have been identified on Annona squamosa (sugar apple), Mimusops roxburghiana (mimusops), Phoenix roebelenii (pygmy date palm), and Ixora spp.

Economic Importance (Back to Top)

The fig wax scale has been reported as a pest of citrus in Italy (Talhouk 1975). Infrequent major local infestations in the citrus-growing areas of Italy have been controlled with refined petroleum oils (Barbagallo 1981). Similar outbreaks occurring in the Aegean Islands, Greece, have been controlled by the application of oils in the summer. Presence of the parasites Coccophagus lycimnia Walker (Aphelinidae) and Scutellista cyanea Motschulsky (Pteromalidae) aid in fig wax scale control (Argyriou and Mourikis 1981).

Management (Back to Top)

Selected References (Back to Top)

  • Argyriou LC, Mourikis PA. 1981. Current status of citrus pests in Greece. Proceedings of the International Society of Citriculture 2: 623-627.
  • Barbagallo S. 1981. Integrated control of citrus pests in Italy. Proceedings of the International Society of Citriculture 2: 620-623.
  • Ben-Dov Y. 1993. A Systematic Catalogue of the Soft Scale Insects of the World. Sandhill Crane Press, Inc., Gainesville, FL. Flora and Fauna Handbook No. 9. 536 pp.
  • Bodkin GE. 1927. The fig wax scale (Ceroplastes rusci L.) in Palestine. Bulletin of Entomological Research 17: 259-263.
  • CABI. (2011). Ceroplastes rusci. Distribution Maps of Plant Pests. (25 September 2012).
  • Hodges A, Hodges G, Buss LJ, Osborne L. (2005). Mealybugs and mealybug look-alikes of the southeastern United States. North Central IPM Center. (6 May 2020)
  • Swailem SM, Awadallah KT. 1973. On the seasonal abundance of the insect and mite fauna on the leaves of sycamore fig trees. Bulletin de la Société Entomologique d'Egypte 57: 1-8.
  • Talhouk AMS. 1975. Citrus pests throughout the world. Ciba-Geigy Agrochemicals, Basel, Switzerland. Technical Monograph No. 4. 21 pp.
  • Vu NT, Eastwood R, Nguyen CT, Pham LV. 2006. The fig wax scale Ceroplastes rusci (Linnaeus) (Homoptera: Coccidae) in south-east Vietnam: Pest status, life history and biocontrol trials with Eublemma amabilis Moore (Lepidoptera: Noctuidae). Entomological Research 36: 196-201.

Authors: Avas B. Hamon (retired) and Gregor J. Mason, Florida Department of Agriculture and Consumer Services (FDACS), Division of Plant Industry.
Originally published as DPI Entomology Circular 380.
Photographs: Doug Caldwell, University of Florida Division of Plant Industry
Web Design: Don Wasik, Jane Medley
Publication Number: EENY-187
Publication Date: January 2001. Latest revision: July 2014. Reviewed: May 2020.

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Featured Creatures Editor and Coordinator: Dr. Elena Rhodes, University of Florida

Canine and Feline Gastroenterology

A comprehensive reference standard for the discipline, Canine and Feline Gastroenterology covers the biology, pathobiology, and diagnosis and treatment of diseases of the gastrointestinal, pancreatic, and hepatobiliary systems. An international team of experts, including 85 authors from 17 different countries, led by Robert Washabau and Michael Day, covers everything from minor problems such as adverse food reactions to debilitating inflammatory, infectious, metabolic, and neoplastic diseases of the digestive system. This authoritative text utilizes an evidence-based approach to reflect the latest science and research, complemented by principles of problem solving, algorithms to improve clinical diagnoses, and extensive full-color illustrations. For generalists and specialists alike, this gastroenterology reference should be part of every serious practitioner's professional library.

A comprehensive reference standard for the discipline, Canine and Feline Gastroenterology covers the biology, pathobiology, and diagnosis and treatment of diseases of the gastrointestinal, pancreatic, and hepatobiliary systems. An international team of experts, including 85 authors from 17 different countries, led by Robert Washabau and Michael Day, covers everything from minor problems such as adverse food reactions to debilitating inflammatory, infectious, metabolic, and neoplastic diseases of the digestive system. This authoritative text utilizes an evidence-based approach to reflect the latest science and research, complemented by principles of problem solving, algorithms to improve clinical diagnoses, and extensive full-color illustrations. For generalists and specialists alike, this gastroenterology reference should be part of every serious practitioner's professional library.

Watch the video: blastx phantom give aimbot (November 2021).