Information

Finding All of the Genes in a given Genome


I'm interested in finding the start position of each nucleotide in a given genome. I first went to EcoCyc and wrote a scraping script for their E coli data, but I can't find the same web page layout for other specimen. So wondering if there is a database with more consistent formatting and such things. I've been looking around BLAST (?), but got confused with the resources, and rather than spinning my wheels, thought I'd ask for some pointers where to start.


I would suggest using the FTP at NCBI to download GFF files for whatever organisms you are interested in, as suggested by commenters. GFF files will have annotations for the genome for genes and also for many other features (figure 1).

Not entirely clear about the goals of this, but if you want annotated coordinates of things in genomes GFFs are a good place to start.

Note that many/most genomes will not have assemblies at the chromosome level, but only at the scaffold/contig level. Depending on your application that may be an issue.

If you want to go to a specific genome/organism, you can search it here. You can access the FTP through the interface for each genome there.


Genetic Maps

Observations that certain traits were always linked and certain others were not linked came from studying the offspring of crosses between parents with different traits. For example, in garden pea experiments, researchers discovered, that the flower’s color and plant pollen’s shape were linked traits, and therefore the genes encoding these traits were in close proximity on the same chromosome. We call exchanging DNA between homologous chromosome pairs genetic recombination , which occurs by crossing over DNA between homologous DNA strands, such as nonsister chromatids. Linkage analysis involves studying the recombination frequency between any two genes. The greater the distance between two genes, the higher the chance that a recombination event will occur between them, and the higher the recombination frequency between them. Figure shows two possibilities for recombination between two nonsister chromatids during meiosis. If the recombination frequency between two genes is less than 50 percent, they are linked.

Crossover may occur at different locations on the chromosome. Recombination between genes A and B is more frequent than recombination between genes B and C because genes A and B are farther apart. Therefore, a crossover is more likely to occur between them.

The generation of genetic maps requires markers, just as a road map requires landmarks (such as rivers and mountains). Scientists based early genetic maps on using known genes as markers. Scientists now use more sophisticated markers, including those based on non-coding DNA, to compare individuals’ genomes in a population. Although individuals of a given species are genetically similar, they are not identical. Every individual has a unique set of traits. These minor differences in the genome between individuals in a population are useful for genetic mapping purposes. In general, a good genetic marker is a region on the chromosome that shows variability or polymorphism (multiple forms) in the population.

Some genetic markers that scientists use in generating genetic maps are restriction fragment length polymorphisms (RFLP), variable number of tandem repeats (VNTRs), microsatellite polymorphisms, and the single nucleotide polymorphisms (SNPs). We can detect RFLPs (sometimes pronounced “rif-lips”) when the DNA of an individual is cut with a restriction endonuclease that recognizes specific sequences in the DNA to generate a series of DNA fragments, which we can then analyze using gel electrophoresis. Every individual’s DNA will give rise to a unique pattern of bands when cut with a particular set of restriction endonucleases. Scientists sometimes refer to this as an individual’s DNA “fingerprint.” Certain chromosome regions that are subject to polymorphism will lead to generating the unique banding pattern. VNTRs are repeated sets of nucleotides present in DNA’s non-coding regions. Non-coding, or “junk,” DNA has no known biological function however, research shows that much of this DNA is actually transcribed. While its function is uncertain, it is certainly active, and it may be involved in regulating coding genes. The number of repeats may vary in a population’s individual organisms. Microsatellite polymorphisms are similar to VNTRs, but the repeat unit is very small. SNPs are variations in a single nucleotide.

Because genetic maps rely completely on the natural process of recombination, natural increases or decreases in the recombination level given genome area affects mapping. Some parts of the genome are recombination hotspots whereas, others do not show a propensity for recombination. For this reason, it is important to look at mapping information developed by multiple methods.


Gene space completeness in complex plant genomes

Historical perspective shows an increase in annotated genome complexity in plants.

Gene completeness of different gene types is a multifaceted problem.

Genome completeness generally improves over multiple consecutive annotations.

Partial gene models continue to be a challenge even for model organisms.

Genome annotations offer ample opportunities to study gene functions, biochemical and regulatory pathways, or quantitative trait loci in plants. Determining the quality and completeness of a genome annotation, and maintaining the balance between them, are major challenges, even for genomes of well-studied model organisms. In this review, we present a historical overview of the complexity in different plant genomes and discuss the hurdles and possible solutions in obtaining a complete and high-quality genome annotation. We illustrate there is no clear-cut answer to solve these challenges for different gene types, but provide tips on guiding the iterative process of generating a superior genome annotation, which is a moving target as our knowledge about plant genomics increases and additional data sources become available.


88 Mapping Genomes

By the end of this section, you will be able to do the following:

  • Define genomics
  • Describe genetic and physical maps
  • Describe genomic mapping methods

Genomics is the study of entire genomes, including the complete set of genes, their nucleotide sequence and organization, and their interactions within a species and with other species. Genome mapping is the process of finding the locations of genes on each chromosome. The maps that genome mapping create are comparable to the maps that we use to navigate streets. A genetic map is an illustration that lists genes and their location on a chromosome. Genetic maps provide the big picture (similar to an interstate highway map) and use genetic markers (similar to landmarks). A genetic marker is a gene or sequence on a chromosome that co-segregates (shows genetic linkage) with a specific trait. Early geneticists called this linkage analysis. Physical maps present the intimate details of smaller chromosome regions (similar to a detailed road map). A physical map is a representation of the physical distance, in nucleotides, between genes or genetic markers. Both genetic linkage maps and physical maps are required to build a genome’s complete picture. Having a complete genome map of the genome makes it easier for researchers to study individual genes. Human genome maps help researchers in their efforts to identify human disease-causing genes related to illnesses like cancer, heart disease, and cystic fibrosis. We can use genome mapping in a variety of other applications, such as using live microbes to clean up pollutants or even prevent pollution. Research involving plant genome mapping may lead to producing higher crop yields or developing plants that better adapt to climate change.

Genetic Maps

The study of genetic maps begins with linkage analysis , a procedure that analyzes the recombination frequency between genes to determine if they are linked or show independent assortment. Scientists used the term linkage before the discovery of DNA. Early geneticists relied on observing phenotypic changes to understand an organism’s genotype. Shortly after Gregor Mendel (the father of modern genetics) proposed that traits were determined by what we now call genes, other researchers observed that different traits were often inherited together, and thereby deduced that the genes were physically linked by their location on the same chromosome. Gene mapping relative to each other based on linkage analysis led to developing the first genetic maps.

Observations that certain traits were always linked and certain others were not linked came from studying the offspring of crosses between parents with different traits. For example, in garden pea experiments, researchers discovered, that the flower’s color and plant pollen’s shape were linked traits, and therefore the genes encoding these traits were in close proximity on the same chromosome. We call exchanging DNA between homologous chromosome pairs genetic recombination , which occurs by crossing over DNA between homologous DNA strands, such as nonsister chromatids. Linkage analysis involves studying the recombination frequency between any two genes. The greater the distance between two genes, the higher the chance that a recombination event will occur between them, and the higher the recombination frequency between them. (Figure) shows two possibilities for recombination between two nonsister chromatids during meiosis. If the recombination frequency between two genes is less than 50 percent, they are linked.


The generation of genetic maps requires markers, just as a road map requires landmarks (such as rivers and mountains). Scientists based early genetic maps on using known genes as markers. Scientists now use more sophisticated markers, including those based on non-coding DNA, to compare individuals’ genomes in a population. Although individuals of a given species are genetically similar, they are not identical. Every individual has a unique set of traits. These minor differences in the genome between individuals in a population are useful for genetic mapping purposes. In general, a good genetic marker is a region on the chromosome that shows variability or polymorphism (multiple forms) in the population.

Some genetic markers that scientists use in generating genetic maps are restriction fragment length polymorphisms (RFLP), variable number of tandem repeats (VNTRs), microsatellite polymorphisms , and the single nucleotide polymorphisms (SNPs). We can detect RFLPs (sometimes pronounced “rif-lips”) when the DNA of an individual is cut with a restriction endonuclease that recognizes specific sequences in the DNA to generate a series of DNA fragments, which we can then analyze using gel electrophoresis. Every individual’s DNA will give rise to a unique pattern of bands when cut with a particular set of restriction endonucleases. Scientists sometimes refer to this as an individual’s DNA “fingerprint.” Certain chromosome regions that are subject to polymorphism will lead to generating the unique banding pattern. VNTRs are repeated sets of nucleotides present in DNA’s non-coding regions. Non-coding, or “junk,” DNA has no known biological function however, research shows that much of this DNA is actually transcribed. While its function is uncertain, it is certainly active, and it may be involved in regulating coding genes. The number of repeats may vary in a population’s individual organisms. Microsatellite polymorphisms are similar to VNTRs, but the repeat unit is very small. SNPs are variations in a single nucleotide.

Because genetic maps rely completely on the natural process of recombination, natural increases or decreases in the recombination level given genome area affects mapping. Some parts of the genome are recombination hotspots whereas, others do not show a propensity for recombination. For this reason, it is important to look at mapping information developed by multiple methods.

Physical Maps

A physical map provides detail of the actual physical distance between genetic markers, as well as the number of nucleotides. There are three methods scientists use to create a physical map: cytogenetic mapping, radiation hybrid mapping, and sequence mapping. Cytogenetic mapping uses information from microscopic analysis of stained chromosome sections ((Figure)). It is possible to determine the approximate distance between genetic markers using cytogenetic mapping, but not the exact distance (number of base pairs). Radiation hybrid mapping uses radiation, such as x-rays, to break the DNA into fragments. We can adjust the radiation amount to create smaller or larger fragments. This technique overcomes the limitation of genetic mapping, and we can adjust the radiation so that increased or decreased recombination frequency does not affect it. Sequence mapping resulted from DNA sequencing technology that allowed for creating detailed physical maps with distances measured in terms of the number of base pairs. Creating genomic libraries and complementary DNA (cDNA) libraries (collections of cloned sequences or all DNA from a genome) has sped the physical mapping process. A genetic site that scientists use to generate a physical map with sequencing technology (a sequence-tagged site, or STS) is a unique sequence in the genome with a known exact chromosomal location. An expressed sequence tag (EST) and a single sequence length polymorphism (SSLP) are common STSs. An EST is a short STS that we can identify with cDNA libraries, while we obtain SSLPs from known genetic markers, which provide a link between genetic and physical maps.


Genetic and Physical Maps Integration

Genetic maps provide the outline and physical maps provide the details. It is easy to understand why both genome mapping technique types are important to show the big picture. Scientists use information from each technique in combination to study the genome. Scientists are using genomic mapping with different model organisms for research. Genome mapping is still an ongoing process, and as researchers develop more advanced techniques, they expect more breakthroughs. Genome mapping is similar to completing a complicated puzzle using every piece of available data. Mapping information generated in laboratories all over the world goes into central databases, such as GenBank at the National Center for Biotechnology Information (NCBI). Researchers are making efforts for the information to be more easily accessible to other researchers and the general public. Just as we use global positioning systems instead of paper maps to navigate through roadways, NCBI has created a genome viewer tool to simplify the data-mining process.

How to Use a Genome Map Viewer

Problem statement: Do the human, macaque, and mouse genomes contain common DNA sequences?

To test the hypothesis, click this link.

In Search box on the left panel, type any gene name or phenotypic characteristic, such as iris pigmentation (eye color). Select the species you want to study, and then press Enter. The genome map viewer will indicate which chromosome encodes the gene in your search. Click each hit in the genome viewer for more detailed information. This type of search is the most basic use of the genome viewer. You can also use it to compare sequences between species, as well as many other complicated tasks.

Is the hypothesis correct? Why or why not?

Online Mendelian Inheritance in Man (OMIM) is a searchable online catalog of human genes and genetic disorders. This website shows genome mapping information, and also details the history and research of each trait and disorder. Click this link to search for traits (such as handedness) and genetic disorders (such as diabetes).

Section Summary

Genome mapping is similar to solving a big, complicated puzzle with pieces of information coming from laboratories all over the world. Genetic maps provide an outline for locating genes within a genome, and they estimate the distance between genes and genetic markers on the basis of recombination frequencies during meiosis. Physical maps provide detailed information about the physical distance between the genes. The most detailed information is available through sequence mapping. Researchers combine information from all mapping and sequencing sources to study an entire genome.


DNA and RNA Extraction

To study or manipulate nucleic acids, the DNA or RNA must first be isolated or extracted from the cells. Various techniques are used to extract different types of DNA (Figure 1). Most nucleic acid extraction techniques involve steps to break open the cell and use enzymatic reactions to destroy all macromolecules that are not desired (such as degradation of unwanted molecules and separation from the DNA sample). Cells are broken using a lysis buffer (a solution which is mostly a detergent) lysis means “to split.” These enzymes break apart lipid molecules in the cell membranes and nuclear membranes. Macromolecules are inactivated using enzymes such as proteases that break down proteins, and ribonucleases (RNAses) that break down RNA. The DNA is then precipitated using alcohol. Human genomic DNA is usually visible as a gelatinous, white mass. The DNA samples can be stored frozen at –80°C for several years.

Figure 1. This diagram shows the basic method used for extraction of DNA.

RNA analysis is performed to study gene expression patterns in cells. RNA is naturally very unstable because RNAses are commonly present in nature and very difficult to inactivate. Similar to DNA, RNA extraction involves the use of various buffers and enzymes to inactivate macromolecules and preserve the RNA.

Figure 2. Shown are DNA fragments from seven samples run on a gel, stained with a fluorescent dye, and viewed under UV light. (credit: James Jacob, Tompkins Cortland Community College)


Cancer Proteomics

Genomes and proteomes of patients suffering from specific diseases are being studied to understand the genetic basis of the disease. The most prominent disease being studied with proteomic approaches is cancer. Proteomic approaches are being used to improve screening and early detection of cancer this is achieved by identifying proteins whose expression is affected by the disease process. An individual protein is called a biomarker, whereas a set of proteins with altered expression levels is called aprotein signature. For a biomarker or protein signature to be useful as a candidate for early screening and detection of a cancer, it must be secreted in body fluids, such as sweat, blood, or urine, such that large-scale screenings can be performed in a non-invasive fashion. The current problem with using biomarkers for the early detection of cancer is the high rate of false-negative results. A false negative is an incorrect test result that should have been positive. In other words, many cases of cancer go undetected, which makes biomarkers unreliable. Some examples of protein biomarkers used in cancer detection are CA-125 for ovarian cancer and PSA for prostate cancer. Protein signatures may be more reliable than biomarkers to detect cancer cells. Proteomics is also being used to develop individualized treatment plans, which involves the prediction of whether or not an individual will respond to specific drugs and the side effects that the individual may experience. Proteomics is also being used to predict the possibility of disease recurrence.

The National Cancer Institute has developed programs to improve the detection and treatment of cancer. The Clinical Proteomic Technologies for Cancer and the Early Detection Research Network are efforts to identify protein signatures specific to different types of cancers. The Biomedical Proteomics Program is designed to identify protein signatures and design effective therapies for cancer patients.


Cancer Proteomics

Genomes and proteomes of patients suffering from specific diseases are being studied to understand the genetic basis of the disease. The most prominent disease being studied with proteomic approaches is cancer. Proteomic approaches are being used to improve screening and early detection of cancer this is achieved by identifying proteins whose expression is affected by the disease process. An individual protein is called a biomarker , whereas a set of proteins with altered expression levels is called a protein signature . For a biomarker or protein signature to be useful as a candidate for early screening and detection of a cancer, it must be secreted in body fluids, such as sweat, blood, or urine, such that large-scale screenings can be performed in a non-invasive fashion. The current problem with using biomarkers for the early detection of cancer is the high rate of false-negative results. A false negative is an incorrect test result that should have been positive. In other words, many cases of cancer go undetected, which makes biomarkers unreliable. Some examples of protein biomarkers used in cancer detection are CA-125 for ovarian cancer and PSA for prostate cancer. Protein signatures may be more reliable than biomarkers to detect cancer cells. Proteomics is also being used to develop individualized treatment plans, which involves the prediction of whether or not an individual will respond to specific drugs and the side effects that the individual may experience. Proteomics is also being used to predict the possibility of disease recurrence.

The National Cancer Institute has developed programs to improve the detection and treatment of cancer. The Clinical Proteomic Technologies for Cancer and the Early Detection Research Network are efforts to identify protein signatures specific to different types of cancers. The Biomedical Proteomics Program is designed to identify protein signatures and design effective therapies for cancer patients.


Less about more about metabolism

You would imagine that the microbial world, which I work with, is much simpler and better-defined than the world of multicellular eukaryotes, yet there is an enormous amount we don’t understand about the simplest microbes and how they function, and the answers must lie to some extent in those parts of the genome whose functions we we still don’t know. So the fundamental open questions about the unknown parts of genomes will greatly limit other approaches to understanding how even the simplest of cells work, and a couple of examples come to mind from my own research. One is from our attempts to reconstruct global metabolism from what we know of genome sequences. This involves applying algorithms to information from genome maps to construct metabolic pathways so that we can try to predict what will happen if you grow the organism in a particular way, or perturb it in some way. It turns out they’re all quite limited, and again one reason for this must be in part the information in the genome that is not being incorporated because we don’t know what it means.

The second example is another -omic approach, metabolomics, which is aimed at identifying all the metabolites in a given cell. But even in the simplest cell you can see perhaps 2,000 metabolite peaks identified by mass spectroscopy, of which we can recognize perhaps 10%. In one sense, it is extraordinarily enlightening to realize how little we really understand biological systems, again even in the simplest cell. You have to wonder how we are ever possibly going to understand the systems biology of a human cell, whether it’s in the brain or the liver or the big toe, with this elephant in the room of genomic information that we don’t understand.


Biology Homework Chapter 12: DNA Profiling and Genomics

Textbook assignment: Chapter 12: DNA Technology and Genomics, sections 11-21.

Study Notes
  • 12.11 DNA sequencing and matching (sometimes called DNA fingerprinting) can identify individuals as the source of cells (like blood or hair) at a crime scene they can also be used to identify inheritance through family trees.
  • 12.12: The polymerase chain reaction (PCR) can be used to produce milliions of copies of a specific DNA fragment for study.
  • 12.13: In gel electrophoresis, DNA segments cut using specific restriction enzymes can be "sorted" by passing electric current through a gel containing the segments. The lighter segments travel further than those of greater weight, effectively sorting the sequence by length.
  • 12.14: Short tandem repeats (STRs) are nucleotide sequences that repeat over and over in the inron between actual genes. The number of repeats can vary in individual humans. Analyzing DNA for STRs at multiple sites allows forensic specialists to identify the individual who is the source of the DNA.
  • 12.15: DNA profiling techniques can be used not only to place individuals at a crime scene, but also to identify victims of disasters where traditional methods are not possible, and to solve "cold" cases or compare DNA evidence from ancient sources with modern species.
  • 12.16: Single nucleotide polymorphisms (SNPs) are differences in individual base pairs that occur in a given gene. When these occur at a restriction site, they can alter the ability of a restriction enzyme to cut the DNA, changing the length of DNA fragments produced by a given restriction enzyme. These differences or restriction fragment length polymorphisms (RLFPs) can also be used to rapidly determine whether DNA samples match or are different.
  • 12.17: Genomics studies complete genomes &mdash the complete DNA sequences for all chromosomes for a particular organism. Having complete sequences allows us to compare genes for specific protein production, and identify similarities in genetic makeup between different organisms.
  • 12.18 The Human Genome project was designed to identify all the genes in human chromosomes.
  • 12.19: The whole genome shotgun method allows researchers to chop an entire human DNA genome into restriction fragments and analyze them using computer techniques. While the method is relatively quick and cheap, there are some problems.
  • 12.20: Proteomics looks at the range of proteins actually produced by a given genome. These proteins can reveal difference in the overall DNA structure (including histone interference) not present in the DNA helix alone.
  • 12.21 Comparisons of genomes are used by biologists to determine how closely related organisms might be. We'll see how these comparisons feed into evolutionary theories.

Web Lecture

Read the following weblecture before chat: Gene Sequencing and the Human Genome

Take notes on any questions you have, and be prepared to discuss the lecture in chat.

Study Activity

Perform the study activity below:

Chat Preparation Activities

  • Essay question: The Moodle forum for the session will assign a specific study question for you to prepare for chat. You need to read this question and post your answer before chat starts for this session.
  • Mastery Exercise: The Moodle Mastery exercise for the chapter will contain sections related to our chat topic. Try to complete these before the chat starts, so that you can ask questions.

Chapter Quiz

  • Required: Complete the Mastery Exercise with a score of 85% or better.
  • Optional: Test yourself with the textbook multiple choice questions and note any that you miss that still don't make sense. Bring questions to chat!
  • Go to the Moodle and take the quiz for this chapter.

Lab Work

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Watch the video: Falco - Jeanny Video (January 2022).