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Bioinformatics Discovery Of Non-coding RNAs
Non-coding RNAs have been discovered using both experimental and bioinformatic approaches. Bioinformatic approaches can be divided into three main categories. The first involves homology search, although these techniques are by definition unable to find new classes of ncRNAs. The second category includes algorithms designed to discover specific types of ncRNAs that have similar properties. Finally, some discovery methods are based on very general properties of RNA, and are thus able to discover entirely new kinds of ncRNAs. Discovery by homology search Homology search refers to the process of searching a sequence database for RNAs that are similar to already known RNA sequences. Any algorithm that is designed for homology search of nucleic acid sequences can be used, e.g., BLAST. However, such algorithms typically are not as sensitive or accurate as algorithms specifically designed for RNA. Of particular importance for RNA is its conservation of a secondary structure, which ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Non-coding RNA
A non-coding RNA (ncRNA) is a functional RNA molecule that is not translated into a protein. The DNA sequence from which a functional non-coding RNA is transcribed is often called an RNA gene. Abundant and functionally important types of non-coding RNAs include transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), as well as small RNAs such as microRNAs, siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and the long ncRNAs such as Xist and HOTAIR. The number of non-coding RNAs within the human genome is unknown; however, recent transcriptomic and bioinformatic studies suggest that there are thousands of non-coding transcripts. Many of the newly identified ncRNAs have not been validated for their function. There is no consensus in the literature on how much of non-coding transcription is functional. Some researchers have argued that many ncRNAs are non-functional (sometimes referred to as "junk RNA"), spurious transcriptions. Others, however, disagree, arguing instead that many ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Locality Sensitive Hashing
In computer science, locality-sensitive hashing (LSH) is an algorithmic technique that hashes similar input items into the same "buckets" with high probability. (The number of buckets is much smaller than the universe of possible input items.) Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from conventional hashing techniques in that hash collisions are maximized, not minimized. Alternatively, the technique can be seen as a way to reduce the dimensionality of high-dimensional data; high-dimensional input items can be reduced to low-dimensional versions while preserving relative distances between items. Hashing-based approximate nearest neighbor search algorithms generally use one of two main categories of hashing methods: either data-independent methods, such as locality-sensitive hashing (LSH); or data-dependent methods, such as locality-preserving hashing (LPH). Definitions An ''LSH family' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CyVA-1 RNA Motif
The CyVA-1 RNA motif is a conserved RNA structure that was discovered by bioinformatics. CyVA-1 motifs are found in Cyanobacteria, Acidobacteriota, and Verrucomicrobiota. Only one example of the RNA is known in any Acidobacterial organism, and only one CyVA-1 RNA was found in any Verrucomicrobial organism. This could suggest that the RNA is not well-established in these bacterial lineages, or simply reflect the fact that relatively few genome In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA (or RNA in RNA viruses). The nuclear genome includes protein-coding genes and non-coding ge ... sequences are available for organisms in these phyla. CyVA-1 RNAs likely function in ''trans'' as sRNAs, and organisms commonly have 2 or 3 separate copies of the CyVA-1 RNA motif in their genomes. References {{reflist Non-coding RNA ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ARRPOF RNA Motif
The ARRPOF RNA motif is a conserved RNA structure that was discovered by bioinformatics. Most ARRPOF motif examples are found in environmental DNA samples, and the host organism is unknown. The only classified organism with an ARRPOF motif is Fusobacterium nucleatum, and the ARRPOF motif instance is found in the pKH9 plasmid. It is hypothesized that the ARRPOF motif is associated with plasmid replication. The pKH9 plasmid has a ''rep'' gene. Although these genes are known to be necessary for the replication of some plasmids, when this ''rep'' gene was eliminated from the pKH9 plasmid, the plasmid was still able to replicate. Another region of the pKH9 plasmid was found that was necessary for replication, and an ARRPOF motif occurs in part of this region. Because plasmid replication involves DNA molecules that are transiently single-stranded, it was also suggested that it is possible that the ARRPOF motif functions as single-stranded DNA and not as RNA. In terms of second ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AbiF RNA Motif
The ''abiF'' RNA motif is a conserved RNA Ribonucleic acid (RNA) is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and deoxyribonucleic acid ( DNA) are nucleic acids. Along with lipids, proteins, and carbohydra ... structure that was discovered by bioinformatics. References {{Reflist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intrinsic Termination
Intrinsic, or rho-independent termination, is a process in prokaryotes to signal the end of transcription and release the newly constructed RNA molecule. In prokaryotes such as E. coli, transcription is terminated either by a rho-dependent process or rho-independent process. In the Rho-dependent process, the rho-protein locates and binds the signal sequence in the mRNA and signals for cleavage. Contrarily, intrinsic termination does not require a special protein to signal for termination and is controlled by the specific sequences of RNA. When the termination process begins, the transcribed mRNA forms a stable secondary structure hairpin loop, also known as a Stem-loop. This RNA hairpin is followed by multiple uracil nucleotides. The bonds between uracil and adenine are very weak. A protein bound to RNA polymerase (nusA) binds to the stem-loop structure tightly enough to cause the polymerase to temporarily stall. This pausing of the polymerase coincides with transcription of the po ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Promoter (genetics)
In genetics, a promoter is a sequence of DNA to which proteins bind to initiate transcription of a single RNA transcript from the DNA downstream of the promoter. The RNA transcript may encode a protein (mRNA), or can have a function in and of itself, such as tRNA or rRNA. Promoters are located near the transcription start sites of genes, upstream on the DNA (towards the 5' region of the sense strand). Promoters can be about 100–1000 base pairs long, the sequence of which is highly dependent on the gene and product of transcription, type or class of RNA polymerase recruited to the site, and species of organism. Promoters control gene expression in bacteria and eukaryotes. RNA polymerase must attach to DNA near a gene for transcription to occur. Promoter DNA sequences provide an enzyme binding site. The -10 sequence is TATAAT. -35 sequences are conserved on average, but not in most promoters. Artificial promoters with conserved -10 and -35 elements transcribe more slowly. All D ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support Vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Covariation (RNA)
In probability theory and statistics, covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the lesser values (that is, the variables tend to show similar behavior), the covariance is positive. In the opposite case, when the greater values of one variable mainly correspond to the lesser values of the other, (that is, the variables tend to show opposite behavior), the covariance is negative. The sign of the covariance therefore shows the tendency in the linear relationship between the variables. The magnitude of the covariance is not easy to interpret because it is not normalized and hence depends on the magnitudes of the variables. The normalized version of the covariance, the correlation coefficient, however, shows by its magnitude the strength of the linear relation. A distinction must be made between (1) the covariance of two random ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nucleotide
Nucleotides are organic molecules consisting of a nucleoside and a phosphate. They serve as monomeric units of the nucleic acid polymers – deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), both of which are essential biomolecules within all life-forms on Earth. Nucleotides are obtained in the diet and are also synthesized from common nutrients by the liver. Nucleotides are composed of three subunit molecules: a nucleobase, a five-carbon sugar (ribose or deoxyribose), and a phosphate group consisting of one to three phosphates. The four nucleobases in DNA are guanine, adenine, cytosine and thymine; in RNA, uracil is used in place of thymine. Nucleotides also play a central role in metabolism at a fundamental, cellular level. They provide chemical energy—in the form of the nucleoside triphosphates, adenosine triphosphate (ATP), guanosine triphosphate (GTP), cytidine triphosphate (CTP) and uridine triphosphate (UTP)—throughout the cell for the many cellular func ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MicroRNA
MicroRNA (miRNA) are small, single-stranded, non-coding RNA molecules containing 21 to 23 nucleotides. Found in plants, animals and some viruses, miRNAs are involved in RNA silencing and post-transcriptional regulation of gene expression. miRNAs base-pair to complementary sequences in mRNA molecules, then gene silence said mRNA molecules by one or more of the following processes: (1) cleavage of mRNA strand into two pieces, (2) destabilization of mRNA by shortening its poly(A) tail, or (3) translation of mRNA into proteins. This last method of gene silencing is the least efficient of the three, and requires the aid of ribosomes. miRNAs resemble the small interfering RNAs (siRNAs) of the RNA interference (RNAi) pathway, except miRNAs derive from regions of RNA transcripts that fold back on themselves to form short hairpins, whereas siRNAs derive from longer regions of double-stranded RNA. The human genome may encode over 1900 miRNAs, although more recent analysis suggests that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bioinformatic
Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for ''in silico'' analyses of biological queries using computational and statistical techniques. Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms (SNPs). Often, such identification is made with the aim to better understand the genetic basis of disease, unique adaptations, desirable properties (esp. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
