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Laura Harring Biography |
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Home > Actresses > H > Harring, Laura > Biography |
Birth Name: Laura Harring |
Born: 03/03/1964 |
Birth Place: Mexico |
Herring was born on March 3, 1964 and raised in Los Mochis, Mexico. After her parents' divorce and mother's remarriage, her family relocated to Texas. Shortly after settling in San Antonio, Harring was the victim of a drive-by shooting when she was 12, suffering a head wound. Following her recovery, she was educated at... |
When she was called upon to crown her successor, a sharp-eyed produced noticed her charisma and beauty and offered her a chance to act in a TV-movie. Harring made her feature acting debut in the forgettable horror sequel, "Silent Night Deadly Night 3: Better Watch Out!" (1989), and followed it up with the role of a Bra... |
In 1999, Harring was on to bigger and better things, landing what she had hoped would be her breakthrough - the part of a mysterious amnesiac who is befriended by a perky aspiring actress in Lynch's proposed TV series, "Mulholland Dr." While she had to bide her time until the project found its ultimate form as a typica... |
The following year, she was cast in the rat-themed horror remake "Willard," which starred Crispin Glover as the title character. In "The Punisher" (2004) - the second big-screen adaptation of Marvel Comics' gun-toting anti-hero - Harring was again a lovely vision onscreen and displayed a provocative and simmering chemi... |
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Open Access Methodology article |
Identifying elemental genomic track types and representing them uniformly |
Sveinung Gundersen1, Matúš Kalaš23, Osman Abul4, Arnoldo Frigessi56, Eivind Hovig178 and Geir Kjetil Sandve8* |
Author Affiliations |
1 Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway |
2 Computational Biology Unit, Uni Computing, Thormøhlensgate 55, 5008 Bergen, Norway |
3 Department of Informatics, University of Bergen, Thormøhlensgate 55, 5008 Bergen, Norway |
4 TOBB University of Economics and Technology, Ankara, Turkey |
5 Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway |
6 Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Blindern, 0317 Oslo, Norway |
7 Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway |
8 Department of Informatics, University of Oslo, Blindern, 0316 Oslo, Norway |
For all author emails, please log on. |
BMC Bioinformatics 2011, 12:494 doi:10.1186/1471-2105-12-494 |
Received:11 May 2011 |
Accepted:30 December 2011 |
Published:30 December 2011 |
© 2011 Gundersen et al; licensee BioMed Central Ltd. |
With the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological conte... |
We here identify intrinsic distinctions between genomic features, and argue that the distinctions imply that a certain variation in the representation of features as genomic tracks is warranted. Four core informational properties of tracks are discussed: gaps, lengths, values and interconnections. From this we delineat... |
The defined track types are shown to capture relevant distinctions between genomic annotation tracks, resulting in varying representational needs and analysis possibilities. The proposed formats, GTrack 1.0 and BioXSD 1.1, cater to the identified track distinctions and emphasize preciseness, flexibility and parsing con... |
Recent ChIP and high-throughput sequencing technologies are currently generating functional annotations at unprecedented speed and resolution. The availability of detailed protein binding locations, DNA methylation, histone modifications, DNA variations of individuals, and more for different tissues and conditions, pro... |
Several efforts have been attempted at defining general formats for the textual representation of genome annotation data. One such format is the General Feature Format (GFF), currently in version 3 [2]. Other generic formats are provided in connection to the UCSC Genome Browser [3], the Browser Extensible Data format (... |
Another reason behind the proliferation of formats seems to be an issue of practicality. Certain types of genome annotations, or genomic tracks, are more efficiently and elegantly represented by certain data formats. Consider a track of DNA melting temperatures, i.e. an algorithmic prediction of the denaturation temper... |
Expanding on this notion of systematic distinctions between track data, it seems that such distinctions also warrant differences in which analyses are applicable. It is for instance meaningful to ask whether SNPs fall inside exons, but it is not meaningful to ask whether SNPs fall inside melting temperature. Conversely... |
In this paper, we start with a clarification of basic nomenclature. We then discuss how the presence of different core informational properties of a track can be used to delineate fifteen different types of tracks at an abstract level. The fifteen track types encompass most existing data formats, in addition to open up... |
Results and Discussion |
A reference genome may be abstracted as a line-based coordinate system. To build on this powerful metaphor, we use the term genomic track (or, in short, track, as used by the UCSC Genome Browser [3]) to refer to a series of data units positioned on such a line. The basic informational unit is called a track element, th... |
We further define a genome feature as a track element or set of track elements comprising a biological unit, e.g. a specific gene, of a certain feature type, e.g. genes. The term biological unit is to be understood broadly and should also include experimental results, algorithmic predictions and similar concepts, such ... |
Core informational properties of tracks |
A genomic track consists of a set of track elements and, for each element, describes a set of properties, such as an identifier, a quality score or the method used. The positional information of a track element is obligatory for any genomic track and can be interpreted generically across tracks. The position of a track... |
A genomic track may also carry a main value associated with each track element, for instance the measured expression of a gene or the copy number of a genomic region. We thus include values among the core informational properties. This main value can be a number (e.g. the expression of a gene), a binary value (e.g. if ... |
Lastly, a track element may be connected to other track elements located at different locations on the genome. This is critical for three-dimensional tracks, as locations that seem far apart when the DNA is unwound, could still be co-located in the nucleus. The corresponding core informational property of a track is th... |
Fifteen genomic track types |
All four core informational properties (gaps, lengths, values, and interconnections) will not always be defined for a track. Consider, for instance, a track of viral insertion points on a genome. As it makes no sense to talk about the length of an insertion point, such a track will not have the lengths property defined... |
Four core properties, being defined or not, gives 24 = 16 distinct combinations. Assuming that a genomic track always consists of track elements with the same core properties, we can distinguish tracks on the basis of which combination of core properties are defined. For one of the sixteen combinations, no core propert... |
Looking closely at the fifteen combinations, an interesting pattern appears. Figure 1 shows an illustration of the informational contents of each combination. As every combination denotes a particular geometric configuration, strikingly distinct from the others, we refer to tracks of the different combinations as havin... |
thumbnailFigure 1. Illustration of the geometric properties of the fifteen track types. The base line is a genome, or a sequence, on which the tracks are defined. Vertical lines represents positions, while horizontal lines represent the lengths of the track elements. Gaps are thus illustrated by any empty areas between... |
Looking at the top left of Figure 1 and going downward, we start at the base case where the only core informational property is the gaps between the track elements. In this case, each track element represents an exact base pair location on the genome, denoting e.g. viral insertion sites. We call this track type Points ... |
Moving on, we remove the values and gaps properties, leaving only lengths. Such tracks consist of segments covering all base pairs of the genome, i.e. a partition of the genome into potentially unequal pieces. Hence, the track type is called Genome Partition (GP). Basic examples of this track type are the partition of ... |
The fourth core informational property, interconnections, can be envisioned as an orthogonal extension to the previous discussion. Adding interconnections, or edges, to the seven track types previously outlined (first column in Figure 1) defines linked versions of the same track types, e.g. Linked Segments (LS) or Link... |
To complete the picture, a last track type needs to be defined. If only the interconnections core property is defined, track elements do not have gaps between them, lengths, or values. All base pairs are then track elements, with each base pair connected to other base pairs by edges, hence the name Linked Base Pairs (L... |
thumbnailFigure 2. Four-dimensional matrix mapping the relations of the fifteen track types. Each dimension represents the exclusion (0) or inclusion (1) of one of the four core informational properties: gaps, lengths, values and interconnections. The track type abbreviations in the top-left box are: Genome Partition (... |
Formal model of genomic tracks |
Formally, we base the discussion of track types on a specific mathematical model of genomic tracks. We treat the genomic coordinates as forming a discrete metric space on the natural numbers, defined by the discrete metric d: |
The genomic coordinates in the model are thus isolated points. A segment or interval starting at a position a and ending at b is defined as the subset S of natural numbers where: |
The length of a segment is defined by the metric d, and is equal to the number of elements in the set. The length of the segment S(1, 3) = {1, 2, 3} is thus d(1, 3) = |1 - 3| + 1 = 3 = |S(1, 3)|. Transferred to the biological domain, the length of a segment is the number of base pairs covered by the segment. The end po... |
From the set notation follows that a point P can be precisely defined as falling inside a segment S if and only if P S. Two segments, on the other hand, may partially overlap. A function is precisely defined as a mathematical function from genomic coordinates to corresponding values, e.g. f = ℕ → ℝ. A step function is ... |
Analysis dependency on track types |
As each of the fifteen track types implies a set of core informational properties, a track type also poses a limit to which analyses are appropriate for a track. It makes sense to calculate the base pair coverage of a track of genes (type: segments), but not for a track of SNPs (type: valued points), which should inste... |
Table 1. Relation between analyses and track types |
Existing representational formats |
Existing formats for representing genomic tracks can broadly be divided into three groups: textual formats, binary formats, and XML formats. Often textual and binary formats are closely connected, such as the SAM and BAM formats for read alignments [11]. This duality is due to the different advantages of the two forms.... |
The large majority of formats for genomic data are textual, and the large majority of the textual data formats are tabular, that is, they consist of tab-separated columns. Three of the most common tabular formats are Generic Feature Format (GFF) [2], Browser Extensible Data format (BED) [4] and Wiggle Track Format (WIG... |
thumbnailFigure 3. Overview of three common tabular formats. A) Generic Feature Format (GFF). The example file is a reduced version of the main example of the GFF version 3 specification [2]. B) Browser Extensible Data format (BED). The example file is fetched from the specification of the format at UCSC [4]. C) Wiggle... |
A main reason for the popularity of tabular formats is that they are inherently simple to create and read, both manually and by computers. This has been a major asset in the field of bioinformatics because of the widespread use of both ad hoc scripting and WYSIWYG editing in spreadsheet software (such as Microsoft Exce... |
XML formats represent a way of letting go of the entire process of custom and explicit parsing of files. In particular when an XML format is specified by a dedicated XML Schema (abbreviated XSD, from XML Schema Definition), the data included in an XML document can be automatically transformed into convenient runtime da... |
Binary formats are often used internally in software systems, and not necessarily provided as public formats. Some exceptions to this are the aforementioned BAM, as well as the bigBed and bigWig formats [18]. The last two formats are binary versions of the BED and WIG format, respectively, providing efficient storage a... |
As Figure 3 illustrates, different formats support different combinations of the core informational properties, and hence, different track types. Table 2 provides an overview of which of the basic track types are covered by some common formats. As each of the different groups of formats (tabular, XML, and binary) has a... |
Table 2. The track types supported by existing tabular, binary and XML formats |
GTrack: Type-aware tabular format |
We here introduce a new tabular track format: the GTrack format, short for both "Genomic Track" and "Generic Track". The GTrack format supports all fifteen previously defined track types, illustrated in Figures 1 and 2. A GTrack file includes a column specification line, specifying the names of all the columns in the f... |
Table 3. Overview of the reserved columns in the GTrack format and their associations to track type |
• Gaps are implicitly represented by the start column, i.e. it holds the start coordinate of a track element and thus marks the end of any preceding gap. |
• For sparse track types, i.e. track types with gaps, length is implicitly represented by the difference between start and end columns. For dense track types (without gaps), there is no start column. The length is then the difference between the previous end position and the current. Deriving length from the end positi... |
• Although several columns in a data set may contain values of potential interest, one of these columns will typically provide a main value used in processing or analysis according to a given purpose. This focus is specified by the value column. |
• The edges column contains, for each track element, a comma-separated list of id's of other track elements which are interconnected with the element in question, in addition to values associated to the edges, e.g. weights or edge types |
• A GTrack file may contain several columns containing values or edges. Users may then switch between them by simply editing the column specification line. |
The edges column requires that the non-core reserved column id is present, containing a unique identifier for each track element. Three other non-core columns are specified in the GTrack format: genome, seqid and strand (see Table 3). The titles of the eight reserved columns are reserved words in the column specificati... |
thumbnailFigure 4. GTrack example files. A) GTrack version of the GFF file in Figure 3A. GTrack conversions of GFF vary according to the set of attributes present in the GFF file. The column selected as the main value may also be changed. B1 and B2) Two possible GTrack conversions of the BED file in Figure 3B. In the d... |
When creating the GTrack format, we have emphasized simplicity, both for creation, manual reading and automated parsing of the format. We have identified three principles towards simplicity: independence of data lines, overview of structural characteristics and equally sized lines. |
The principle of independent data lines states that it should be possible to interpret each data line in a tabular format independently of its location in the file. This is a principle followed in many common formats, e.g. GFF [2] or BED [4]. Following this principle gives several advantages. First, when creating or ma... |
The principle of including an overview of structural characteristics means that a track file should start with a set of configurable options that describe the structure of the data lines, in an easily readable manner. Note that many of these characteristics will, by nature, include redundant information, i.e. that coul... |
Table 4. Overview of the header variables of the GTrack format |
The principle of equally sized lines states that all data lines contain the same number of columns, i.e. that all attributes have a value. Columns that do not contain information are marked with a period character. There are several advantages for this solution compared to the solution used in the GFF format, where the... |
In addition to simplicity, the GTrack format aims at being highly extensible and inter-operable. First, the ability to define columns in any order and number, provides ample options for extensibility, in addition to simplifying conversion. In many cases, converting another tabular format to GTrack is as simple as addin... |
thumbnailFigure 5. GTrack subtype example. A) An ad hoc GTrack suptype specification based on the example GTrack file in Figure 4A, which is a conversion from the GFF file in Figure 3A. This and other GTrack subtypes are available from the GTrack website [20]. B) A minimal GTrack header, parsable by fully compliant GTr... |
Additional file 1. GTrack specification. Specification document of GTrack 1.0. |
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