Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
In the field of bioinformatics, a sequence database is a type of biological database that is composed of a large collection of computerized ("digital") nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer. The UniProt database is an example of a protein sequence database. As of 2013 it contained over 40 million sequences and is growing at an exponential rate.[1] Historically, sequences were published in paper form, but as the number of sequences grew, this storage method became unsustainable.
Searching in a sequence database involves looking for similarities between a genomic/protein sequence and a query string and, finding the sequence in the database that "best" matches the target sequence (based on criteria which vary depending on the search method). The number of matches/hits is used to formulate a score that determines the similarity between the sequence query and the sequences in the sequence database.[2] The main goal is to have a good balance between the two criteria.
The need for sequence databases originated in 1950 when Frederick Sanger reported the primary structure of insulin. He won his second Nobel Prize for creating methods for sequencing nucleic acids, and his comparative approach is what sparked other protein biochemists to begin collecting amino acid sequences. Thus marking the beginning of molecular databases.[3]
In 1965 Margaret Dayhoff and her team at the National Biomedical Research Foundation (NBRF) published "The Atlas of Protein Sequence and Structure". They put all know protein sequences in the Atlas, even unpublished material. This can be seen as the first attempt to create a molecular database. They made use of the newly computerized (1964) Medical Literature Analysis and Retrieval System (MEDLARS) at the National Institutes of Health (NIH). The team used computers to store the data but had to manually type and proofread each sequence, which had a high cost in time and money.[3]
In 1966 the team released the second edition of the Atlas, double the size of the first. It contained about 1000 sequences, and this time was coined as an information explosion. The NBRF was on the cutting edge of utilizing computers for medicine and biology at this time. Dayhoff and her team made use of their facilities for determining amino acid sequences of protein molecules in mainframe computers. The number of discovered sequences continued to grow allowing for a deeper comparative analysis of proteins than ever before. This led to many developments such as, probabilistic models of amino acid substitutions, sequence aligning and phylogenetic trees of evolutionary relationships of proteins.[3]
The entire sequencing process became fully automated.[3]
The first nucleotide sequence database was created. Previously known as the European Molecular Biology Laboratory (EMBL) Nucleotide Sequence Data Library (now known as European Nucleotide archive). Human Genome Project began in 1988. The project's goal was sequence and map all the genes in a human which required the capability to create and utilize a large sequence database.[4]
We now have many sequence databases, tools for using them and easy access to them. One of the largest being GenBank which contains over 2 billion sequences.[3]
Records in sequence databases are deposited from a wide range of sources, from individual researchers to large genome sequencing centers. As a result, the sequences themselves, and especially the biological annotations attached to these sequences, may vary in quality. There is much redundancy, as multiple labs may submit numerous sequences that are identical, or nearly identical, to others in the databases.[5]
Many sequence annotations are based not on laboratory experiments, but on the results of sequence similarity searches for previously annotated sequences. Once a sequence has been annotated based on similarity to others, and itself deposited in the database, it can also become the basis for future annotations. This can lead to a transitive annotation problem because there may be several such annotation transfers by sequence similarity between a particular database record and actual wet lab experimental information.[6] Therefore, care must be taken when interpreting the annotation data from sequence databases.
Most of the current database search algorithms rank alignment by a score, which is usually a particular scoring system.[7] The solution towards solving this issue is found by making a variety of scoring systems available to suit to the specific problem.
Searching algorithm often produce an ordered list lacking biological significance.[8]
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In the field of bioinformatics, a sequence database is a type of biological database that is composed of a large collection of computerized ("digital") nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer. The UniProt database is an example of a protein sequence database. As of 2013 it contained over 40 million sequences and is growing at an exponential rate. Historically, sequences were published in paper form, but as the number of sequences grew, this storage method became unsustainable.
The International Nucleotide Sequence Database Collaboration (INSDC) consists of a joint effort to collect and disseminate databases containing DNA and RNA sequences. It involves the following computerized databases: NIG's DNA Data Bank of Japan (Japan), NCBI's GenBank (United States) and the EMBL-EBI's European Nucleotide Archive. New and updated data on nucleotide sequences contributed by research teams to each of the three databases are synchronized on a daily basis through continuous interaction between the staff at each of the collaborating organizations. All of the data in INSDC is available for free and unrestricted access, for any purpose, with no restrictions on analysis, redistribution, or re-publication of the data. This policy has been a foundational principle of the INSDC since its inception. Since the 1990s, most of the world's major scientific journals have required that sequence data be deposited in an INSDC database as a pre-condition for publication. The Data Bank of Japan/GenBank/EMBL-EBI synchronization is maintained according to a number of guidelines which are produced and published by an International Advisory Board. The guidelines consist of a common definition of the feature tables for the databases, which regulate the content and syntax of the database entries, in the form of a common DTD (Document Type Definition). The syntax is called INSDSeq and its core consists of the letter sequence of the gene expression (amino acid sequence) and the letter sequence for nucleotide bases in the gene or decoded segment. In a DBFetch operation shows a typical INSD entry at the EMBL-EBI database; the same entry at NCBI.
PSSRdb (Polymorphic Simple Sequence Repeats database) is a database of polymorphic simple sequence repeats
The Sequence Read Archive (SRA, previously known as the Short Read Archive) is a bioinformatics database that provides a public repository for DNA sequencing data, especially the "short reads" generated by high-throughput sequencing, which are typically less than 1,000 base pairs in length. The archive is part of the International Nucleotide Sequence Database Collaboration (INSDC), and run as a collaboration between the NCBI, the European Bioinformatics Institute (EBI), and the DNA Data Bank of Japan (DDBJ). The archive was established by the National Center for Biotechnology Information (NCBI) in 2007 in order to provide a repository for data produced by RNA-Seq and ChIP-Seq studies as well as large-scale studies including the Human Microbiome Project and the 1000 Genomes Project. Originally called the Short Read Archive, the name was changed in anticipation of future sequencing technologies being able to produce longer sequence reads. The volume of data deposited in the Sequence Read Archive has grown rapidly. As of September 2010, 65% of the SRA was human genomic sequence, with another 16% relating to human metagenome sequence reads. Much of this data was deposited through the 1000 Genomes Project. In June 2011, the data contained within the SRA passed 100 Terabases of DNA in volume. The preferred data format for files submitted to the SRA is the BAM format, which is capable of storing both aligned and unaligned reads. Internally the SRA relies on the NCBI SRA Toolkit, used at all three INSDC member databases, to provide flexible data compression, API access and conversion to other formats such as FASTQ. NCBI announced their plan to close the NCBI SRA in February 2011 due to funding reduction. However, EBI and DDBJ announced that they would continue to support the SRA. In October 2011, NCBI announced continuation of funding for the SRA. Deposition of data in the SRA is mandated by most funding agencies and open access journals. Nature Publishing Group journals require that DNA and RNA sequencing data is made available through the SRA.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Brown v. Board of Education in 1954.
